Systems and methods for generating tables from print-ready digital source documents

ABSTRACT

Systems and methods are provided for generating tables from print-ready digital source documents. A document is received and one or more text fragments are identified on a rendered page of the document. A wrapping region collection is generated, comprising one or more wrapping regions. A tabular, narrative and label score is generated for each wrapping region. A block type is assigned to each wrapping region based on the scores. A wrapping region group and a block set are generated. One or more tables are generated based on text fragments corresponding to one of the one or more blocks. The text fragments are organized into corresponding fields of the one or more tables.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation of U.S. patent applicationSer. No. 15/612,979, filed Jun. 2, 2017, (now U.S. Pat. No. 10,289,670),which is a Continuation of U.S. patent application Ser. No. 14/993,988,filed Jan. 12, 2016, (now U.S. Pat. No. 9,703,766), which areincorporated herein by reference in their entirety.

FIELD OF THE INVENTION

The present invention relates generally to generating tables and moreparticularly to systems and methods for generating tables fromprint-ready digital source documents.

BACKGROUND

The digital world has given rise to the rapid growth and expansion ofdata that is generated, stored, analyzed, and used by a variety ofentities including companies, organizations, universities, andindividuals. Data is continuously being generated and organized intodocuments by millions of users and their devices, such as mobiledevices, computers, wearable devices, point of sale terminals,navigation devices, and a multitude of sensors stored thereon.

Often, data is compiled, aggregated and/or stored in print-ready digitalsource documents of file types such as XPS, RTF, PDF and the like.Print-ready digital source documents typically include a multitude ofunstructured, semi-structured and/or structured data that is distributedonto fixed locations of a rendered page, rather than into organizedlines, rows, cells, or the like. In other words, data on print-readydigital source documents is not organized relative to each other, but isinstead fixedly arranged with relation to coordinates of a renderedpage.

Print-ready digital source documents are used (e.g., generated,transmitted, stored) in just about any conceivable context or industry,including government, healthcare, education, retail, manufacturing,financial services, telecom, and the like. Print-ready digital sourcedocuments are used, for example, to store information, fix informationonto rendered pages, printing information, and send information withoutrisking that information being displaced throughout the pages of thedocument.

The data in the print-ready digital source documents is difficult toaccess because it is arranged in a non-tabular format, which does notenable it to be easily selected, sorted, modified, charted, and thelike. One common theme among entities and individuals generating andusing print-ready digital source documents is the desire to make theirdata more easily accessible, for example, so that it can be analyzed,filtered and used to efficiently and effectively generate tables. This,in turn, makes print-ready digital source document data easier andquicker to consume (e.g., to generate tables), less prone to errors, andmore reliable.

There is a need, therefore, for systems and methods that allow forprint-ready digital source documents files containing tabular data to beused to generate tables, spreadsheets, and the like. There is also aneed for systems and methods that identify relationships between data,classifies data, and aggregates portions of data based on perceivedrelationship between them. Moreover, there is a need for such systemsand methods to be executed with minimal user interaction.

SUMMARY

The example embodiments and implementations presented herein meet theabove-identified needs by providing systems and methods forautomatically creating tables using auto-generated templates.

In some example embodiments, a method is provided for generating tablesfrom print-ready digital source documents. The method comprisesreceiving (e.g., from memory, over a network), by a processor of acomputing device, a print-ready digital source document (e.g., XPS, RTF,PDF), the digital source document comprising at least one rendered page;identifying, by the processor, one or more text fragments in the atleast one rendered page, each of the text fragments comprising text,spatial coordinates indicating the positioning of the text fragment onthe rendered page, and an index assigned based on the spatialcoordinates on the rendered page (e.g., starting from top left of page,moving left to right and top to bottom); generating, by the processor, awrapping region collection comprising one or more wrapping regions,wherein each of the wrapping regions comprises one or more fragmentruns, and wherein each of the one or more fragment runs comprises asubset of the one or more text fragments that are adjacent to oneanother and within a predetermined horizontal separation threshold and avertical separation threshold; calculating, by the processor, for eachof the one or more wrapping regions of the wrapping region collection, atabular score, a narrative score, and a label score, indicating howclosely each of the one or more wrapping regions is related to a tabularblock type, a narrative block type and a label block type, respectively;assigning, by the processor, a block type (e.g., tabular, narrative,label) to each of the one or more wrapping regions based on thecorresponding calculated tabular score, narrative score and label score;generating a wrapping region group set comprising one or more wrappingregion groups, wherein each of the one or more wrapping region groupscomprises a subset of the one or more wrapping regions that arespatially related to one another (e.g., horizontally, left to right);generating, by the processor, a block set comprising one or more blocks,wherein each of the one or more blocks comprises a subset of the one ormore wrapping region groups that are spatially related to one another(e.g., vertically, top to bottom); and generating, by the processor, oneor more tables, each of the one or more tables comprising the textfragments corresponding to one of the one or more blocks, wherein eachof the one or more tables comprises the corresponding text fragmentseach organized into corresponding fields (e.g., cells, arranged by rowand column) of the one or more tables.

In some example embodiments, generating each of the one or more wrappingregions in the wrapping region collection comprises: identifying, by theprocessor, a first text fragment (e.g., based on the index of the textfragments, starting with first indexed text fragment; text fragment i)from among the one or more text fragments; assigning, by the processor,a current text fragment flag to the first text fragment, the currenttext fragment flag indicating a single one of the one or more textfragments being processed; generating, by the processor, a currentwrapping region and a current fragment run; adding, by the processor,the first text fragment having the current text fragment flag assignedthereto to the current fragment run; identifying, by the processor, asecond text fragment (e.g., based on the index of the text fragments;the next text fragment on the rendered page; text fragment i+1) fromamong the one or more text fragments, the second text fragment beinghorizontally adjacent (e.g., from left to right) to the first textfragment having the current text fragment flag assigned thereto, withinthe predetermined horizontal separation threshold; assigning, by theprocessor, the current text fragment flag to the second text fragment;adding, by the processor, the second text fragment having the currenttext fragment flag assigned thereto to the current fragment run; adding,by the processor, the current fragment run to the end of the currentwrapping region (e.g., thereby compiling a first line of text fragmentsin a table), wherein the current wrapping region comprises a boundingbox comprising borders matching outer borders of fragment runs comprisedtherein (e.g., expanded each time the current fragment run is added);identifying, by the processor, a third text fragment (e.g., based on theindex of the text fragments; the next text fragment on the renderedpage; text fragment i+1) from among the one or more text fragments, thethird text fragment being the leftmost of the one or more text fragmentsthat is within the predetermined vertical separation threshold and thepredetermined horizontal separation threshold of a bottom border of thebounding box of the current wrapping region; assigning, by theprocessor, the current text fragment flag to the third text fragment;removing, by the processor, the contents of the current fragment run;adding, by the processor, the third text fragment having the currenttext fragment flag assigned thereto to the current fragment run;identifying, by the processor, a fourth text fragment (e.g., based onthe index of the text fragments; the next text fragment on the renderedpage; text fragment i+1) from among the one or more text fragments, thefourth text fragment being horizontally adjacent (e.g., from left toright) to the third text fragment having the current text fragment flagassigned thereto, within the predetermined horizontal separationthreshold; assigning, by the processor, the current text fragment flagto the fourth text fragment; adding, by the processor, the fourth textfragment having the current text fragment flag assigned thereto to thecurrent fragment run; and adding, by the processor, the current fragmentrun to the end of the current wrapping region (e.g., thereby compiling asecond line of text fragments for the table).

In some example embodiments, identifying the third text fragment isperformed in response to identifying the absence of other text fragmentsfrom among the one or more text fragments that are horizontally adjacent(e.g., from left to right) to the second text fragment.

In some example embodiments, the tabular score, the narrative score andthe label score of each of the one or more wrapping regions arecalculated based on one or more attributes selected from the groupconsisting of (i) a normalization ratio, (ii) a density ratio, (iii) analignment ratio, (iv) a capital or non-alphabetic ratio, (v) a textfragment quantity, and (vi) a bold count.

In some example embodiments, the block type assigned to each of the oneor more wrapping regions corresponds to the highest of the tabularscore, the narrative score, and the label score calculated for therespective wrapping region.

In some example embodiments, the normalization ratio indicates a degreeof normalized fragments among the subset of the one or more textfragments corresponding to each of the one or more wrapping regions,wherein the density ratio indicates a density value of fragments amongthe subset of the one or more text fragments corresponding to each ofthe one or more wrapping regions, wherein the alignment ratio indicatesthe degree of aligned text fragments among the subset of the one or moretext fragments corresponding to each of the one or more wrappingregions, wherein the capital or non-alphabetic ratio indicates a degreeof text fragments, among the subset of the one or more text fragmentscorresponding to each of the one or more wrapping regions that beginwith either a capital letter or a non-alphabetic character, wherein thetext fragment quantity indicates a number of text fragments among thesubset of the one or more text fragments corresponding to each of theone or more wrapping regions, and wherein the bold count indicates anumber of text fragments among the subset of the one or more textfragments corresponding to each of the one or more wrapping regions thatcomprise bold text.

In some example embodiments, a high normalization ratio negativelyimpacts a corresponding tabular score, positively impacts acorresponding narrative score, and positively impacts a correspondinglabel score, wherein a high density ratio negatively impacts acorresponding tabular score, positively impacts a correspondingnarrative score, and positively impacts a corresponding label score,wherein a high alignment ratio positively impacts a correspondingtabular score, and negatively impacts a corresponding narrative score,wherein a high capital or non-alphabetic ratio positively impacts acorresponding tabular score, and negatively impacts a correspondingnarrative score, wherein a high text fragment quantity negativelyimpacts a corresponding label score, and wherein a high bold countpositively impacts a corresponding label score.

In some example embodiments, the tabular score, the narrative score andthe label score are values between 0.0 and 1.0, wherein if one of theone or more wrapping regions comprises only a single number fragment,the tabular score of the one of the one or more wrapping regions is 1.0,and wherein if one of the one or more wrapping regions comprises only asingle text fragment, the label score of the one of the one or morewrapping regions is 1.0.

In some example embodiments, the generating the wrapping region groupset comprises: adding, by the processor, each of the one or morewrapping regions to a corresponding one of the one or more wrappingregion groups, wherein each of the one or more wrapping region groupscomprises spatial coordinates indicating the positioning of the one ormore wrapping region groups on the rendered page, and wherein each ofthe one or more wrapping region groups comprises a bounding boxdelineating outer borders of the corresponding wrapping region; adding,by the processor, the one or more wrapping region groups to a coordinatemap based on the spatial coordinates of the one or more wrapping regiongroups; identifying, by the processor, among the one or more wrappingregion groups, a current wrapping region group, the current wrappingregion group being the tallest, uppermost, and leftmost wrapping regiongroup, on the coordinate map, that comprises a tabular block type;identifying, by the processor, a current wrapping region rectangulararea matching the dimensions and spatial position of the bounding box ofthe current wrapping region group; extending, by the processor, the leftand right borders of the current wrapping region rectangular area tomatch the left and right borders of the coordinate map; identifying, bythe processor, one or more intersecting wrapping region groups, amongthe one or more wrapping region groups, that comprise a bounding boxintersecting the current wrapping region rectangular area; calculating,by the processor, for each of the one or more intersecting wrappingregion groups, a corresponding intersecting wrapping region group mergescore; merging, by the processor, with the current wrapping regiongroup, each of the one or more intersecting wrapping region groupscomprising an intersecting wrapping region group merge score higher thana predetermined intersecting wrapping region group merge threshold;removing, by the processor, the current wrapping region group, includingthe merged one or more intersecting wrapping region groups, from thecoordinate map; and adding, by the processor, the current wrappingregion group to the wrapping region group set.

In some example embodiments, each of the intersecting wrapping regiongroup merge scores is calculated based on properties of thecorresponding intersecting wrapping region group and the currentwrapping region group, the properties being selected from the groupconsisting of (i) a vertical alignment, (ii) block type, and (iii)matching lines.

In some example embodiments, the generating the block set comprises:adding, by the processor, each of the one or more wrapping region groupsto a corresponding one of the one or more blocks, wherein each of theone or more blocks comprises spatial coordinates indicating thepositioning of the one or more blocks on the rendered page, and whereineach of the one or more blocks comprises a bounding box delineatingouter borders of the corresponding wrapping region group; adding, by theprocessor, the one or more blocks to the coordinate map based on thespatial coordinates of the one or more blocks; identifying, by theprocessor, among the one or more blocks, a current block, the currentblock being the widest, uppermost and leftmost block on the coordinatemap; identifying, by the processor, a current block rectangular areamatching the dimensions and spatial position of the bounding box of thecurrent block; extending, by the processor, the top and bottomboundaries of the current block rectangular area to match the top andbottom boundaries of the coordinate map; identifying, by the processor,one or more intersecting blocks, among the one or more blocks, thatcomprise a bounding box intersecting the current block rectangular area;calculating, by the processor, for each of the one or more intersectingblocks, a corresponding intersecting block merge score; merging, by theprocessor, with the current block, each of the one or more intersectingblocks comprising an intersecting block merge score higher than apredetermined intersecting block merge threshold; removing, by theprocessor, the current block, including the merged one or moreintersecting blocks, from the coordinate map; and adding, by theprocessor, the current block to the block set.

In some example embodiments, each of the intersecting block merge scoresis calculated based on properties of the corresponding intersectingblock and the current block, the properties being selected from thegroup consisting of: (i) horizontal alignment, (ii) column position,(iii) column alignment, and (iv) column data type.

In some example embodiments, the print-ready digital source document isa fixed-layout file (e.g., PDF, XPS).

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects, features, and advantages ofthe present disclosure will become more apparent and better understoodby referring to the following description taken in conjunction with theaccompanying drawings.

FIG. 1A is a diagram illustrating a system for generating tables fromprint-ready digital source documents, according to an exemplaryembodiment.

FIG. 1B illustrates a flow chart for generating tables from print-readydigital source documents according to an exemplary embodiment.

FIG. 2A illustrates an interface for generating tables from print-readydigital source documents according to an exemplary embodiment.

FIG. 2B illustrates an interface for generating tables from print-readydigital source documents according to an exemplary embodiment.

FIG. 2C illustrates an interface for generating tables from print-readydigital source documents according to an exemplary embodiment.

FIG. 2D illustrates an interface for generating tables from print-readydigital source documents according to an exemplary embodiment.

FIG. 2E illustrates an interface for generating tables from print-readydigital source documents according to an exemplary embodiment.

FIG. 2F illustrates wrapping regions that have been joined due to ahigher vertical separation threshold, according to an exemplaryembodiment.

FIG. 2G(I) illustrates a flow chart for executing a wrapping algorithmaccording to an exemplary embodiment.

FIG. 2G(II) illustrates a flow chart for executing a wrapping algorithmaccording to an exemplary embodiment.

FIG. 3(I) illustrates a first part of a flow chart for identifyingoptimal separation thresholds according to an exemplary embodiment.

FIG. 3(II) illustrates a second part of a flow chart for identifyingoptimal separation thresholds according to an exemplary embodiment.

FIG. 4A illustrates an interface for generating tables from print-readydigital source documents according to an exemplary embodiment.

FIG. 4B illustrates a flow chart for executing a classificationalgorithm, according to an exemplary embodiment.

FIG. 5A(I) illustrates a first part of a flow chart for executing ahorizontal aggregation algorithm or a horizontal aggregation portion ofan aggregation algorithm, according to an exemplary embodiment.

FIG. 5A(II) illustrates a second part of a flow chart for executing ahorizontal aggregation algorithm or a horizontal aggregation portion ofan aggregation algorithm, according to an exemplary embodiment.

FIG. 5B (I) illustrates a first part of a flow chart for executing avertical aggregation algorithm or a vertical aggregation portion of anaggregation algorithm, according to an exemplary embodiment.

FIG. 5B (II) illustrates a second part of a flow chart for executing avertical aggregation algorithm or a vertical aggregation portion of anaggregation algorithm, according to an exemplary embodiment.

FIG. 5C illustrates an interface for generating tables from print-readydigital source documents according to an exemplary embodiment.

FIG. 5D illustrates an interface for generating tables from print-readydigital source documents according to an exemplary embodiment.

FIG. 5E illustrates an interface for generating tables from print-readydigital source documents according to an exemplary embodiment.

FIG. 5F illustrates an interface for generating tables from print-readydigital source documents according to an exemplary embodiment.

FIG. 5G illustrates an interface for generating tables from print-readydigital source documents according to an exemplary embodiment.

FIG. 6 is a block diagram of an example network environment for use inthe methods and systems described herein, according to an illustrativeembodiment.

FIG. 7 is a block diagram of an example computing device and an examplemobile computing device, for use in illustrative embodiments of theinvention.

DETAILED DESCRIPTION

It should be understood that systems, devices, methods, and processes ofthe claimed invention encompass variations and adaptations developedusing information from the embodiments described herein. Adaptationand/or modification of the systems, devices, methods, and processesdescribed herein may be performed by those of ordinary skill in therelevant art.

Throughout the description, where articles, devices, and systems aredescribed as having, including, or comprising specific components, orwhere processes and methods are described as having, including, orcomprising specific steps, it should be understood that, additionally,there are articles, devices, and systems of the present invention thatconsist essentially of, or consist of, the recited components, and thatthere are processes and methods according to the present invention thatconsist essentially of, or consist of, the recited processing steps.

It should be understood that the order of steps or order for performingactions is immaterial so long as the invention remains operable.Moreover, two or more steps or actions may be conducted simultaneously.

The mention herein of any publication or patent application, forexample, in the Background section, is not an admission that suchpublication or patent application constitutes prior art with respect toany of the claims or subject matter presented herein. The Backgroundsection is presented for purposes of clarity and is not intended to be adescription of prior art with respect to any claim.

Definitions

In order for the present disclosure to be more readily understood,certain terms are first defined below. Additional definitions for thefollowing terms and other terms are set forth throughout thespecification.

“Digital published text source” or “print-ready digital sourcedocument”: Any published text that is in digital form and expressed as ametalanguage, where the text content is accessible along with itsspatial location on the rendered page.

“Rendered page”: The print-ready form of a page, where text has beenplaced into the page's coordinate space along with associated attributes(e.g. font face, size, and style).

“Unicode”: A universal character encoding standard for text stored indigital form.

“Whitespace”: Any Unicode character that represents horizontal orvertical space when rendered.

“Spatial coordinates”: The x and y locations used as a spatial referencepoints for text objects (e.g., characters, fragments) on a renderedpage.

“Text fragment”: A string of non-whitespace Unicode characters andassociated spatial coordinates, which locate and/or identify thelocation of the fragment on the rendered page.

“Coordinate map”: A collection of information (e.g., text fragments)that is indexed by its/their spatial coordinates, and ordered byits/their appearance on the rendered page, from top to bottom and leftto right.

“Horizontal separation threshold”: The maximum spatial distance allowedbetween horizontally adjacent text fragments, in order for them to beconsidered part of the same fragment run.

“Fragment run”: A collection of horizontally adjacent text fragmentswhose horizontal separations fall within the horizontal separationthreshold.

“Vertical separation threshold”: The maximum distance allowed betweenthe bottom of the bounding box of a wrapping region and the nextvertically adjacent text fragment below, in order for the fragment to beconsidered part of the wrapping region.

“Wrapping region”: A collection of vertically adjacent fragment runswhose vertical separations fall within the vertical separationthreshold.

“Wrapping region collection”: The set of all wrapping regions present onthe rendered page.

“Bounding box”: A rectangle expressed in spatial coordinates that isused to define the bounds and location of text fragments, fragment runs,wrapping regions, and the like on the rendered page.

“Current text fragment”: The text fragment eligible for inclusion in afragment run.

“Current fragment run”: The fragment run eligible for inclusion in awrapping region.

“Current wrapping region”: The wrapping region eligible for inclusion inthe wrapping region collection.

“Tabular data”: A grouping of structured data in which text fragmentscan be arranged by rows and columns.

“Narrative data”: A grouping of unstructured data that has no tabularformat. Occurs, for example, in the form of sentences and paragraphs.

“Label data”: A single text fragment or grouping of text fragmentsusually found above a section of narrative or tabular data. Label datagives context to related sections of text.

“Block type”: The classification of tabular, narrative, or label, whichcan be applied to a wrapping region or any aggregation of wrappingregions.

“Type score”: A decimal value (e.g., between 0.0 and 1.0) that isproduced by calculating the weighted average of a collection ofsub-scores. This score is used to determine how closely a regionidentifies as a tabular, narrative, or label block type.

“Sub-score”: A fractional value (e.g., between 0.0 and 1.0) ismultiplied by a predetermined weight to generate an associated score(e.g., type score, merge score).

“Normalized text fragment”: A collection of text fragments that arehorizontally separated by no more than the width of a predeterminednumber of characters (e.g., 3 characters). This width is determinedbased on the font attributes of the text fragments (e.g., font face,size, and style).

“Normalization ratio”: The number of normalized text fragments dividedby the total number of text fragments within a wrapping region.

“Density ratio”: The percentage of a wrapping region's bounding box areaoccupied by text fragment bounding boxes.

“Alignment”: Refers to the x-axis and/or y-axis value of the left edge,right edge, top edge, bottom edge, or center point of a bounding box andhow it relates spatially to another bounding box. That is, alignment mayrefer to a horizontal or vertical relationship between two correspondingpoints of two bounding boxes.

“Alignment group”: A collection of normalized text fragments whosebounding boxes are either left, right, or center aligned.

“Alignment ratio”: The number of normalized text fragments that fitwithin at least one alignment group divided by the total number ofnormalized text fragments within a wrapping region.

“Bold count”: The number of text fragments with bolded text.

“Capital or non-alphabetic ratio”: The number of normalized textfragments that start with either a capital letter or a non-alphabeticcharacter divided by the number of normalized text fragments.

“Wrapping region group”: A collection of wrapping regions. In additionto containing wrapping regions, wrapping region groups also contain acollection of lines and a block type derived from its wrapping regions.

“Line”: A collection of horizontally adjacent text fragments that arealigned relative to their font base line. Meant to represent a line oftext or single row of data in a table as would appear on a printed page.Rendered pages do not inherently possess the concept of lines, becausethey are simply fragments of text with coordinate positions.

“Wrapping region group collection”: The set of all wrapping regiongroups present on the rendered page.

“Block column”: A specialized type of wrapping region group thatcontains metadata about the alignment (left, right, center), data type,and line structure of the wrapping regions it contains. The wrappingregions within a block column are arranged vertically.

“Block table”: A collection of adjacent and non-overlapping blockcolumns. The block type, in some example embodiments, is tabular. Inaddition to containing wrapping regions, block tables also contain acollection of block columns created by stripping the wrapping regionsout of the wrapping region groups and arranging them into verticalgroupings.

“Block table collection”: The set of all block tables present on therendered page.

“Merge score”: A decimal value (e.g., between 0.0 and 1.0) produced bycalculating the weighted average of a collection of sub-scores. Thisscore is used to determine the strength of the spatial relationshipbetween two regions.

“Merge threshold”: A predetermined decimal value (e.g., between 0.0 and1.0) that determines the point at which a merge score is high enoughsuch that two wrapping region groups or block tables should be merged.

“Separation thresholds”: The combination of space width threshold, lineseparation threshold, and line affinity ratio used by the wrappingalgorithm to create wrapping regions.

“Space width threshold”: A value (e.g., between 0.0 and 3.0) thatrepresents the maximum amount of space that is allowed between two textfragments in order to join them into the same wrapping region. In someexample embodiments, the space width threshold may have a default value(e.g., 2.5).

“Line separation threshold”: A value (e.g., between 0.0 and 1.0) thatrepresents the maximum amount of vertical space that is allowed betweentwo lines in order to join them into the same wrapping region. In someexample embodiments, the line separation threshold may have a defaultvalue (e.g., 0.5).

“Line affinity ratio”: A value used to determine the maximum ratio ofdifference that can exist between the heights of two text fragments inorder to join them into the same wrapping region. In some exampleembodiments, the line affinity ratio may have a default value (e.g.,0.3).

System

FIG. 1A is a diagram illustrating a system 100A for generating tablesfrom print-ready digital source documents, according to an exemplaryembodiment.

System 100 includes computing devices 101 and 103, which are connectedto a server 107 via a network 105. The server 107 and the computingdevices 101 and 103 may communicate over the network 105 using protocolssuch as Internet Protocol Suite (TCP/IP), HTTP, FTP, IMAP, Fibre ChannelProtocol (FCP), Fibre Channel over Ethernet (FCoE), Internet SCSI(iSCSI), and the like.

In some example implementations, the computing devices 101 and 103include laptops, desktop computers, smartphones, tablets, mobiledevices, wearable devices, workstations, personal digital assistants,mainframes, and the like. The computing devices 101 and 103, and theserver 107 each include software and hardware (e.g., at least oneprocessor and at least one memory).

In some example embodiments, the computing devices 101 and 103 are usedto generate tables from print-ready digital source documents such asXPS, RTF or PDF-type document and/or files. Generating tables isperformed, for example, using a table-generating tool, application, orthe like stored and/or executing on the computing devices 101 and/or103. The table-generating tool, application or the like is programmed toexecute various algorithms, including, for example, a wrappingalgorithm, classification algorithm, aggregation algorithm, thresholdingalgorithm, and the like. Generating tables from print-ready digitalsource documents is explained in more detail below with reference toFIGS. 1B-5. Generally, a table refers to an arrangement of data intorows and columns, cells, fields, or the like.

In some example embodiments, the server 107 is a platform that providesthe functionality of the table-generating tool, application or the liketo the computing devices 101 and 103, for example, via the network 105.This functionality can be provided, for example, as part of asoftware-as-a-service (SaaS), platform-as-a-service (PaaS) orinfrastructure-as-a-service (IaaS) offering or architecture. That is,the computing devices 101 and 103 may store, generate or transmitprint-ready digital source documents to the server 107 for analysis andgeneration of tables. In other example embodiments, the print-readydigital source documents may be generated, transmitted for analysis,and/or used to create tables at the server 107 by the computing devices101 and/or 103, via the network 105, and using an application (e.g., webbrowser application) executing on or accessible by the computing devices101 and/or 103.

Process

FIG. 1B illustrates a flow chart 100B for generating tables fromprint-ready digital source documents according to an exemplaryembodiment. As shown in flow chart 100, at step 152, a print-readydigital source document is received by a computing device (e.g., clientcomputing device, cloud computing device), for example, from a memoryassociated with (e.g., incorporated in, connected to, communicativelycoupled to) the computing device or from another interconnectedcomputing device. For example, the digital source document may beretrieved by the computing device in response to user instructions, ormay be received when transmitted or pushed to it. As described above, aprint-ready digital source document may be an XPS, RTF or PDF-typedocument, and includes one or more rendered pages.

In some example embodiments, the print-ready digital source documentincludes text with identifiable text characters and text fragments. Asdescribed above, a text fragment is a string of non-whitespace Unicodecharacters. Each text character and/or text fragment is associated withspatial coordinates (e.g., X,Y coordinates) identifying itscorresponding location on a rendered page of the print-ready digitalsource document. In this way, each rendered page includes a coordinatemap on which text fragments of the rendered page (collectively “textfragment collection”) are indexed according to their spatialcoordinates, from top to bottom and left to right (e.g., in the mannerin which English language documents are typically read by humans). Forexample, the text fragment at the top left of the rendered page isassigned an index value i=0 The next text fragment to the right of thetext fragment i=0 is assigned an index value i=1 or i+1. The last textfragment indexed is the text fragment at the bottom right portion of therendered page.

In turn, at step 154, the computing device applies a wrapping algorithmto the text fragments of the rendered page to organize the text intospatial regions called “wrapping regions.” That is, wrapping regions areidentified and/or created. A group or set of wrapping regions on arendered page are referred to as a “wrapping region collection.” Thewrapping region algorithm is described in further detail below withreference to FIGS. 2A-2G.

At step 156, the computing device applies a classification algorithm towrapping regions such as the wrapping regions in a wrapping regioncollection. The classification algorithm assigns a type or block type toeach of the wrapping regions. The type or block type identifies and/orindicates the type of data (e.g., text) with which the wrapping regionis associated, including (1) tabular data, (2) narrative data, and (3)label data. In some example embodiments, the type or block type of eachwrapping region is identified by calculating a tabular score, narrativescore and label score and assigning the type based on the identifiedscores. In some example embodiments, the scores are calculated usingvarious features and/or characteristics of the text or text fragments inthe wrapping regions. The classification algorithm is described infurther detail below with reference to FIGS. 4A and 4B.

At step 158, the computing device applies an aggregation algorithm tothe wrapping regions such as the wrapping regions in a wrapping regioncollection. The aggregation algorithm identifies and/or combineswrapping regions that are spatially (e.g., horizontally and vertically)related to one another. In some example embodiments, the wrappingregions that are combined are wrapping regions that are on the samehorizontal plane and/or vertical plane, on a rendered page, as aselected wrapping region. The aggregation algorithm aggregates wrappingregions into wrapping region groups and/or blocks. In some exampleembodiments, a merge score is calculated to determine whether twospatially related wrapping regions should be merged and/or combined. Insome example embodiments, merge scores are calculated using features ofthe wrapping regions as well as characteristics of the relationshipbetween multiple wrapping regions. The aggregation algorithm isdescribed in further detail below with reference to FIGS. 5A-5G.

In turn, at step 160, tables are identified, generated and/or output.The tables include text from a rendered page of the print-ready digitalsource document. The tables include rows and columns. Each intersectionof a row and column on the table is and/or corresponds to a cell orfield of the table. In some example embodiments, each table correspondsto a block identified and/or generated using the aggregation algorithm.Each cell or field on the table includes a text fragment from a block ofthe rendered page on the print-ready digital source document.

FIG. 2A illustrates an interface 200A for generating tables fromprint-ready digital source documents according to an exemplaryembodiment. The interface 200A may be rendered, displayed and/or causedto be displayed via a graphical display (e.g., monitor, screen) of acomputing device. The interface 200A includes a panel, section or area201 in which text (and/or data) 203 of a print-ready digital sourcedocument is displayed. In some example embodiments, the text (and/ordata) includes narrative data, tabular data and/or label data. Althoughthe text (and/or data) 203 in FIG. 2A is an income statement including alarge amount of tabular data, it should be understood that theprint-ready digital source document may include various types ofinformation distributed into any combination of types of data.

Interface 200A also includes a panel with input means such ascheckboxes, slider bars, buttons, radial buttons, and the like, whichare used to set, change and/or input information to be used in theexecution of a wrapping algorithm, classification algorithm, aggregationalgorithm, and/or separation threshold algorithm. For example, the inputmeans may be for setting, changing and/or inputting information such aswhether boxes should be displayed and/or drawn around characters, textfragments, lines, and/or wrapping regions; alignment of text (e.g.,left, center right) and a corresponding tolerance; a tolerance sliderbar for appending text fragments; slider bars for line separation, andthe like. It should be understood that the input means, in some exampleembodiments, are not displayed in the interface 200A and the informationis set, changed and/or input by an administrator.

When a document is opened, imported, retrieved and/or displayed in theinterface 200A, various information is identified and/or calculated. Forexample, text and text fragments in the document are identified, spatialcoordinates of the text fragments on the page are determined and/orcalculated, and bounding boxes are identified and/or drawn for each textfragment. In some example embodiments, hovering a mouse, cursor or thelike over a text fragment causes a bounding box to be displayed (e.g.,temporarily, while mouse or cursor is hovered over the text fragment). Abounding box may be a solid-border box or the like (e.g., dottedrectangle, colored, etc.) that, among other things, identifies the outerboundaries of the text fragment.

FIG. 2B illustrates an interface 200B for generating tables fromprint-ready digital source documents according to an exemplaryembodiment. In FIG. 2B, the print-ready digital document has beenopened, text fragments are identified, and bounding boxes for each textfragment are identified and drawn in the panel 201. As shown in FIG. 2B,each box and/or rectangle is and/or identifies a text fragment. Forexample, in FIG. 2B, two text fragments have been labeled as textfragment 205-1 (“Software”) and text fragment 205-2 (“Licenses”).

Wrapping Algorithm

As described above with reference to FIG. 1B, at step 154, a wrappingalgorithm is applied to text fragments of or on a rendered page of adigital source document to organize the text and/or text fragments intospatial regions called “wrapping regions.”

FIG. 2G illustrates a flow chart 200G for executing a wrapping algorithmaccording to an exemplary embodiment. At step 250, text fragments andtheir associated spatial coordinates are identified on a rendered pageof a print-ready digital source document. In turn, at step 252, theidentified text fragments are added to a coordinate map corresponding tothe rendered page, based on the spatial coordinates of the textfragments. The text fragments are indexed according to their respectivespatial coordinates, ordered by their appearance on the rendered page,from the top to bottom and left to right, such that a topmost andleftmost text fragment is the text fragment with the first index on therendered page and the text fragment to its right is the text fragmentwith the second index on the rendered page. In this way, the textfragment with the last index on the rendered page is the bottommost andrightmost text fragment on the rendered page.

At step 254, the uppermost and leftmost text fragment on the coordinatemap is located and/or identified. At step 256, a determination is madeas to whether an uppermost and leftmost text fragment has been locatedon the coordinate map at step 254. That is, the determination at step256 identifies whether any text fragments remain to be processed. If itis determined at step 256 that an uppermost and leftmost text fragmentwas not located and/or identified at step 254, the wrapping algorithmconcludes and/or determines, at step 257, that the wrapping regioncollection is complete (e.g., that all text fragments on the renderedpage have been assigned to wrapping regions).

On the other hand, if it is determined at step 256 that an uppermost andleftmost text fragment was indeed located and/or identified at step 254,that text fragment (e.g., the uppermost and leftmost text fragmentlocated at step 254) is labeled, assigned, marked and/or flagged as thecurrent text fragment at step 258. In turn, at step 260, an emptywrapping region is created and/or generated, and is also labeled,assigned, marked, and/or flagged as the current wrapping region. At step262, an empty fragment run is created and/or generated, and is alsolabeled, assigned, marked and/or flagged as the current fragment run.

At step 264, the current text fragment (e.g., the text fragment locatedat step 254 and labeled as the current text fragment at step 258) isadded and/or appended to the current fragment run (e.g., the fragmentrun created at step 262).

In turn, at step 266, a next horizontally adjacent text fragment,relative to the current text fragment, is located and/or identified.More specifically, moving from left to right on the x-axis, starting atthe right edge of the current text fragment (e.g., the right edge of thecurrent text fragment's bounding box), the wrapping algorithm searchesfor a text fragment that is within a predetermined horizontal separationthreshold. The horizontal separation threshold is described in furtherdetail below with reference to FIG. 3. The horizontal separationthreshold is or represents a distance within which two text fragmentsmust be located to be considered to be horizontally adjacent to oneanother. A higher horizontal separation threshold allows for moredistant text fragments to be deemed to be horizontally adjacent to oneanother, as compared with a lower horizontal separation threshold.

At step 268, a determination is made as to whether a next horizontallyadjacent text fragment (e.g., a text fragment within the horizontalseparation threshold of the current text fragment) was located and/oridentified at step 266. If it is determined at step 268 that a nexthorizontally adjacent text fragment was indeed located at step 266, thatnext horizontally adjacent text fragment is labeled, assigned, markedand/or flagged as the current text fragment at step 270, therebyreplacing the previously identified current text fragment.

In turn, the wrapping algorithm proceeds at step 264 but with a new textfragment (e.g., the text fragment located at step 266) as the currenttext fragment. That is, the current text fragment identified at step 266and labeled as such at step 270 is added to the current fragment run atstep 264. The wrapping algorithm repeats steps 264, 266, 268 and 270 foras long as horizontally adjacent text fragments are identified and,those identified horizontally adjacent text fragments are added to orappended to the current text fragment run. In this way, the resultingcurrent text fragment run is a set of horizontally adjacent textfragments that are within the horizontal separation threshold.

FIG. 2C illustrates an interface 200C for generating tables fromprint-ready digital source documents according to an exemplaryembodiment. FIG. 2C includes text fragment runs identified and/orhighlighted by a corresponding bounding box or the like. For example, inFIG. 2C, two text fragment runs have been labeled as text fragment run207-1 (“$ Millions”), 207-2 (“Software Licenses”), and 207-3 (“9.9”).That is, in FIG. 2C the individual text fragments 205-1 and 205-2 ofFIG. 2B, which have been determined to be within a horizontal separationthreshold and are therefore deemed to be horizontally adjacent, arecombined to form a fragment run 207-2. As can be seen in FIG. 2C, textfragment “Software License” and “9.9”, which are on the same horizontalX-axis as one another, have not been combined into a single fragment runbecause they are sufficiently separated from one another and thereforeoutside or beyond the horizontal separation threshold.

FIG. 2D illustrates an interface 200D for generating tables fromprint-ready digital source documents according to an exemplaryembodiment. In FIG. 2D, fragment runs are identified by shaded boxes asopposed to the outlined/bordered boxes of FIG. 2C.

Still with reference to FIG. 2G, if it is determined at step 268 that anext horizontally adjacent text fragment was not located at step 266,the current text fragment run is added and/or appended to the end of thecurrent wrapping region, and the contents of the current text fragmentare removed at step 272. This causes the current text fragment to beempty after step 272. In turn, at step 274, a next vertically adjacenttext fragment, relative to the current wrapping region, is locatedand/or identified. More specifically, moving downwards (e.g., from topto bottom) on the Y-axis, starting at the bottom edge of the currentwrapping region, the wrapping algorithm searches for the leftmost textfragment that is within a predetermined vertical separation thresholdand, in some example embodiments, within the horizontal bounds of abounding box of the current wrapping region. The vertical separationthreshold is described in further detail below with reference to FIG. 3.The vertical separation threshold is or represents a distance withinwhich a wrapping region and a text fragment must be located to beconsidered to be vertically adjacent to one another. A higher verticalseparation threshold allows for a more distant text fragment to bedeemed to be vertically adjacent to a wrapping region, compared with alower vertical separation threshold. FIG. 2F illustrates wrappingregions that have been joined due to a higher vertical separationthreshold as compared with FIG. 2E, according to an exemplaryembodiment.

At step 276, a determination is made as to whether a next verticallyadjacent text fragment (e.g., a text fragment within the verticalseparation threshold of the current wrapping region) was located and/oridentified at step 274. If it is determined at step 276 that a nextvertically adjacent text fragment was indeed located at step 274, thenext vertically adjacent text fragment is labeled, assigned, markedand/or flagged as the current text fragment at step 277, therebyreplacing the previous current text fragment.

In turn, the wrapping algorithm proceeds at step 264 but with a new textfragment (e.g., the text fragment located at step 274) as the currenttext fragment. That is, the current text fragment identified at step 274and labeled as such at step 277 is added to the current fragment run atstep 264. The wrapping algorithm repeats steps 264, 266, 268 and 270 foras long as horizontally adjacent text fragments are identified and,those identified horizontally adjacent text fragments are added to orappended to the current text fragment run. In this way, the resultingcurrent text fragment run is a set of horizontally adjacent textfragments that are within the horizontal separation threshold.

FIG. 2E illustrates an interface 200E for generating tables fromprint-ready digital source documents according to an exemplaryembodiment. FIG. 2E includes wrapping regions that are identified and/orhighlighted by a corresponding bounding box or the like. For example, inFIG. 2E, two text fragment runs have been labeled as text fragment run209-1 and 209-2. That is, in FIG. 2E the individual text fragments 205-1and 205-2 of FIG. 2B and/or the text fragment runs 207-1, 207-2 and207-3 of FIG. 2C, which have been determined to be within a verticalseparation threshold and are therefore deemed to be vertically adjacent,are combined into respective wrapping regions. As can be seen in FIG.2E, text fragment run 207-1 and 207-2 of FIG. 2C, which are on the samevertical y-axis as one another and within the vertical separationthreshold, are part of the wrapping region 209-1.

Still with reference to FIG. 2G, if it is determined at step 276 that anext vertically adjacent text fragment was not located at step 274, thecurrent wrapping region is added and/or appended to the wrapping regioncollection at step 278. The text fragments in the current wrappingregion are removed from the coordinate map of the rendered page at step280.

In turn, the wrapping algorithm proceeds back to step 254, in which anuppermost and leftmost text fragment is located and/or identified in thecoordinate map. In each subsequent iteration, the uppermost and leftmosttext fragment located at step 254 is different because text fragmentshave been removed from the coordinate map at step 280. The algorithmcontinues to be executed until the wrapping region collection iscomplete, at step 257.

Separation Threshold Algorithm

FIG. 3 illustrates a flow chart 300 for identifying optimal separationthresholds according to an exemplary embodiment. As described above,horizontal and/or vertical separation thresholds are used during theexecution of a wrapping algorithm to identify text fragments that arehorizontally and/or vertically adjacent, respectively, and can thereforebe joined into fragment runs and/or wrapping regions.

At step 350, a new set of separation thresholds is defined with defaultspace width thresholds and line separation threshold values. In someexample embodiments, the default space width threshold and lineseparation threshold values are multiples of a predetermined value(e.g., 0.25, 0.5). It should be understood that multiples providedherein are exemplary, and the predetermined multiple may be any valuethat functions with the algorithms described herein. The set ofseparation thresholds may include a horizontal separation threshold anda vertical separation threshold. The horizontal separation threshold isassociated with a space width threshold that is or represents themaximum amount of horizontal space on a rendered page that is allowedbetween two text fragments in order to join the text fragments into thesame fragment run and/or wrapping region. In some example embodiments,the default space width threshold defined at step 350 is a predetermineddefault value (e.g., 2.5). It should be understood that default valuesprovided herein are exemplary, and the default values can be any valuethat functions with the algorithms described herein. The verticalseparation threshold is associated with a line separation threshold thatis or represents the maximum amount of vertical space on a rendered pagethat is allowed between two lines (e.g. text fragments, fragment runs)in order to join them into the same wrapping region. In some exampleembodiments, the default line separation threshold defined at step 350is a predetermined default value (e.g., 0.5). It should be understoodthat default values provided herein are exemplary, and the defaultvalues can be any value that functions with the algorithms describedherein.

In turn, at step 352, the space width threshold of the horizontalseparation threshold is decremented by a predetermined value (e.g., 0.5(e.g., starting with the default value (e.g., 2.5) in the firstiteration)). At step 354, a score is calculated for the horizontalseparation threshold. Calculating and/or computing a score (e.g., steps354, 364, 374, 384) is performed based on one or more sub-scorescalculated for the following metrics:

-   -   Wrapping region overlap: High numbers of overlaps between        wrapping regions produced by a set of separation thresholds        negatively impacts the score for those thresholds.    -   Single-line regions: High numbers of single-line wrapping        regions produced by a set of separation thresholds negatively        impacts the score for those thresholds.    -   Multi-line regions: High numbers of multi-line wrapping regions        that do not have any overlaps produced by a set of separation        thresholds positively impacts the score for those thresholds.

In some example embodiments, calculating a sub-score for a wrappingregion overlap metric includes counting and/or calculating the number ofoverlaps between wrapping regions on the rendered (e.g., current) page.The overlaps may be a space where the bounding boxes of two wrappingregions intersect). In turn, the calculated count is divided by thenumber of text fragments on the rendered page to obtain a value (e.g.,between 0.0 and 1.0). This obtained value is subtracted from 1.0. Inturn, the resulting value is multiplied by a predetermined weight (e.g.,3) to produce a corresponding sub-score.

In some example embodiments, calculating a sub-score for a multi-lineregion metric includes initializing a score (e.g., to 0.0). In turn, thewrapping regions on the rendered page that (1) consist of more than onefragment run (e.g., series of text fragments that can be considered tobe on the same line), and (2) that do not intersect with other wrappingregions, are identified and/or located. For each such identifiedwrapping region, add to the score the number of text fragments in eachwrapping region divided by the number of text fragments on the page(i.e., score=score+(fragments_in_each_region/fragments_on_page)). Inturn, the resulting score is divided by the number of text fragments onthe page. The resulting value is multiplied by a predetermined weight(e.g., 1) to produce a corresponding sub-score.

In some example embodiments, calculating a sub-score for a single lineregions metric includes counting and/or calculating the number ofwrapping regions on the rendered page that consist of only one fragmentrun. This count is divided by the number of text fragments on thecurrent page to obtain a value (e.g., between 0.0 and 1.0.). Thisobtained value is subtracted from 1.0. In turn, the resulting value ismultiplied by a predetermined weight (e.g., 1) to produce acorresponding sub-score.

It should be understood that the values described in connection withcalculating the sub-scores are part of an exemplary embodiment, andother values may be used in accordance with the algorithms describedherein.

Still with reference to step 354, in some example embodiments, the score(e.g., block score) is a weighted average of the sub-scores. Forexample, sub-scores may be combined (e.g., to create a score) by takingthe sum of the sub-scores to be combined and dividing that sum by thesum of the weights for the sub-scores (e.g., 3, 1, 1), to produce avalue (e.g., score) between 1.0 and 0.0.

At step 356, the calculated score is analyzed to determine whether it isthe best score calculated during the decrementing of the space widththreshold. If it is determined at step 356 that the score calculated atstep 354 is the best score, that score is stored and/or marked, at step358, as the best score of the space width threshold decrementingprocess. In some example embodiments, a best score is a highest score.On the other hand, if it is determined at step 356 that the scorecalculated at step 354 is not the best score, or once the best score hasbeen stored at step 358, the separation threshold algorithm determines,at step 360, whether the space width threshold is equal to apredetermined minimum value (e.g., 0.0) (e.g., whether the space widththreshold decrementing process has reached an end).

If it is determined at step 360 that the space width threshold is notequal to the predetermined minimum value (e.g., 0.0), the separationthreshold algorithm returns to step 352, where the space width thresholdis decreased by a predetermined multiple or amount (e.g., 0.5), andsteps 354, 356, 358, and 360 are repeated until the space widththreshold has been decremented to the predetermined minimum value (e.g.,0.0). Thus, each time the space width threshold is decremented (e.g., by0.5), a score is calculated and that score is analyzed to determinewhether it is the best score (e.g., by comparing the newly calculatedscore to the best score). If the newly-calculated score is better thanthe best score, the best score is replaced with the newly calculatedscore. At the end of the process, the best score during the decrementingof the space width threshold is stored and made accessible.

In turn, at step 360, if it is determined that the space width thresholdis equal to the predetermined minimum value (e.g., 0.0), the separationthreshold algorithm proceeds to a process of incrementing the spacewidth threshold. That is, at step 362, the space width threshold of thehorizontal separation threshold is incremented by a predeterminedmultiple or amount (e.g., 0.5) (e.g., starting with the default value(e.g., of 2.5) in the first iteration). At step 364, a score iscalculated for the horizontal separation threshold. Calculating and/orcomputing a score is done based on one or more of the metrics discussedabove in connection with step 354.

At step 366, the calculated score is analyzed to determine whether it isthe best (e.g., highest) score calculated during the incrementing of thespace width threshold. If it is determined at step 366 that the scorecalculated at step 364 is the best score, that score is stored and/ormarked, at step 368, as the best score of the space width thresholdincrementing. On the other hand, if it is determined at step 366 thatthe score calculated at step 364 is not the best score, or once the bestscore has been stored at step 368, the separation threshold algorithmdetermines, at step 370, whether the space width threshold is equal tothe predetermined maximum value (e.g., 3.0) (e.g., whether the spacewidth threshold incrementing process has reached an end).

If it is determined at step 370 that the space width threshold is notequal to a maximum predetermined value (e.g., 3.0), the separationthreshold algorithm returns to step 362, where the space width thresholdis increased by a predetermined multiple or amount (e.g., 0.5), andsteps 364, 366, 368, and 370 are repeated until the space widththreshold has been incremented to the predetermined maximum value (e.g.,3.0). Thus, each time the space width threshold is incremented (e.g., by0.5), a score is calculated and that score is analyzed to determinewhether it is the best score of the process of incrementing the spacewidth threshold (e.g., by comparing the newly calculated score to thebest score). If the newly-calculated score is better than the bestscore, the best score of the process of incrementing the space widththreshold is replaced with the newly calculated score. At the end of theprocess, the best (e.g., highest) score during the incrementing of thespace width threshold is stored and made accessible.

In turn, at step 372, a process of decrementing the line separationthreshold of the vertical separation threshold is initiated. Morespecifically, at step 372, the line separation threshold is decrementedby a predetermined multiple or amount (e.g., 0.25) (e.g., starting withthe default value (e.g., of 0.5) in the first iteration). At step 374, ascore is calculated for the vertical separation threshold. Calculatingand/or computing a score is done based on the metrics described above inconnection with step 354.

At step 376, the calculated score is analyzed to determine whether it isthe best score calculated during the decrementing of the line separationthreshold. If it is determined at step 376 that the score calculated atstep 374 is the best score of the process of decrementing the lineseparation threshold, that score is stored and/or marked, at step 378,as the best score of the line separation threshold decrementing. On theother hand, if it is determined at step 376 that the score calculated atstep 374 is not the best score, or if the score is stored as the bestscore in step 378, the separation threshold algorithm determines, atstep 380, whether the line separation threshold is equal to apredetermined minimum value (e.g., 0.0) (e.g., whether the lineseparation decrementing process has reached an end).

If it is determined at step 380 that the line separation threshold isnot equal to 0.0, the separation threshold algorithm returns to step372, where the line separation threshold is decreased by a predeterminedmultiple or amount (e.g., 0.25), and steps 374, 376, 378, and 380 arerepeated until the line separation threshold has been decremented to thepredetermined minimum value (e.g., 0.0.) Thus, each time the lineseparation threshold is decremented (e.g., by 0.25), a score iscalculated and that score is analyzed to determine whether it is thebest score (e.g., by comparing the newly calculated score to the bestscore). If the newly-calculated score is better than the best (e.g.,highest) score, the best score is replaced with the newly calculatedscore. At the end of the process, the best score during the decrementingof the line separation threshold is stored and made accessible.

In turn, at step 380, if it is determined that the line separationthreshold is equal to the predetermined minimum value (e.g., 0.0), theseparation threshold algorithm proceeds to a process of incrementing theline separation threshold. That is, at step 382, the line separationthreshold of the vertical separation threshold is incremented by apredetermined multiple or amount (e.g., 0.25) (e.g., starting with thedefault value (e.g., of 0.5) in the first iteration). At step 384, ascore is calculated for the vertical separation threshold. Calculatingand/or computing a score is done based on one or more of the metricsdiscussed above in connection with step 354.

At step 386, the calculated score is analyzed to determine whether it isthe best (e.g., highest) score calculated during the incrementing of theline separation threshold. If it is determined at step 386 that thescore calculated at step 384 is the best score, that score is storedand/or marked, at step 388, as the best score of the line separationthreshold incrementing. On the other hand, if it is determined at step386 that the score calculated at step 384 is not the best score, or oncethe best score has been stored at step 388, the separation thresholdalgorithm determines, at step 390, whether the line separation thresholdis equal to a predetermined maximum value (e.g., 1.0) (e.g., whether theline separation threshold incrementing process has reached an end).

If it is determined at step 390 that the line separation threshold isnot equal to the predetermined maximum value (e.g., 1.0), the separationthreshold algorithm returns to step 382, where the line separationthreshold is increased by a predetermined multiple or amount (e.g.,0.25), and steps 384, 386, 388, and 390 are repeated until the lineseparation threshold has been incremented to the predetermined maximumvalue (e.g., 1.0). Thus, each time the line separation threshold isincremented (e.g., by 0.25), a score is calculated and that score isanalyzed to determine whether it is the best (e.g., highest) score ofthe process of incrementing the line separation threshold (e.g., bycomparing the newly calculated score to the best score). If thenewly-calculated score is better than the best score, the best score ofthe process of incrementing the line separation threshold is replacedwith the newly calculated score. At the end of the process, the bestscore during the incrementing of the line separation threshold is storedand made accessible.

In turn, at step 392, the best score of each of the space widththreshold decrementing and incrementing processes, and the best score ofeach of the line separation threshold incrementing process areidentified, output, made accessible, or the like.

It should be understood that the default values described above inconnection with FIG. 3 correspond to an exemplary embodiment, andtherefore any default values can be used. Likewise, values by which thespace width threshold and line separation threshold are decremented andincremented, as well as the lowermost and uppermost cutoff values usedduring the decrementing and incrementing of the space width thresholdand line separation threshold may vary.

Classification Algorithm

FIG. 4A illustrates an interface 400A for generating tables fromprint-ready digital source documents according to an exemplaryembodiment. In FIG. 4A, the interface 400A includes and/or displaysvarious text and/or data, each of which is assigned a block type using aclassification algorithm described in further detail below withreference to FIG. 4B. That is, in some example implementations, the textand/or data is organized into wrapping regions, and each wrapping regionis assigned a block type such as narrative data, tabular data or labeldata using the classification algorithm. Once each wrapping region hasbeen categorized and/or assigned a block type, boxes, highlighting orthe like of different colors, shading, border type and the like are usedto visually indicate each wrapping region's corresponding block type.

For example, as shown in FIG. 4A, wrapping region 401 (and otherwrapping regions with the same border (e.g., dashed, dotted,dashed-dotted)) is a wrapping region of block type label, indicatingthat the wrapping region includes label data; wrapping region 403 (andother wrapping regions with the same border) is a wrapping region ofblock type narrative, indicating that the wrapping region includesnarrative data; and wrapping region 405 (and other wrapping regions withthe same border) is a wrapping region of block type tabular, indicatingthat the wrapping region includes tabular data.

FIG. 4B illustrates a flow chart 400B for executing a classificationalgorithm, according to an exemplary embodiment. As shown in FIG. 4B, atstep 450, a wrapping region collection including one or more wrappingregions is retrieved and/or generated (e.g., in accordance with theprocess described above in connection with FIG. 2G). In some exampleembodiments, the wrapping region collection includes wrapping regionscorresponding to a rendered page of a digital-source document. Eachwrapping region in the wrapping region collection includes verticallyadjacent fragment runs, each of which includes one or more textfragments.

In turn, at step 452, for each of the wrapping regions in the wrappingregion collection, a tabular score, a narrative score and a label scoreis calculated. The calculated scores are used to determine the blocktype corresponding to the wrapping region. In some example embodiments,the metrics and ratios described below (and which are also described infurther detail above in the “Definitions” section) are calculated andused to compute sub-scores and, in turn, type scores (e.g., scores).

To produce a tabular sub-score for a wrapping region, the followingmetrics and/or ratios are first calculated:

-   -   Normalization ratio: A high degree of normalized fragments in        the wrapping region has a negative impact on its tabular score.    -   Density ratio: A high fragment density value in the wrapping        region has a negative impact on its tabular score.    -   Alignment ratio: A high degree of aligned (e.g., right, left,        center) text fragments in a wrapping region has a positive        impact on its tabular score.    -   Capital or non-alphabetic ratio: A high degree of normalized        text fragments that begin with either a capital letter or        non-alphabetic character in a wrapping region has a positive        impact on its tabular score.

To produce a narrative sub-score for a wrapping region, the followingmetrics and/or ratios are first calculated:

-   -   Normalization ratio: A high degree of normalized fragments in        the wrapping region has a positive impact on its narrative        score.    -   Density ratio: A high fragment density value in the wrapping        region has a positive impact on its narrative score.    -   Alignment ratio: A high degree of aligned text fragments in the        wrapping region has a negative impact on its narrative score.    -   Capital or non-alphabetic ratio: A high degree of normalized        text fragments that begin with either a capital letter or        non-alphabetic character in the wrapping region has a negative        impact on its narrative score

To produce a label sub-score for a wrapping region, the followingmetrics and/or ratios are first calculated:

-   -   Normalization ratio: A high degree of normalized fragments in        the wrapping region has a positive impact on its label score.    -   Density ratio: A high fragment density value in the wrapping        region has a positive impact on its label score.    -   Text fragment quantity: A high number of text fragments in the        wrapping region has a negative impact on its label score.    -   Bold count: A high number of bold text fragments in the wrapping        region has a positive impact on its label score.

In some example embodiments, to calculate a normalization ratio, thenumber or count of fragment runs in a wrapping region are calculated. Inturn, the calculated count of wrapping regions is divided by the numberof text fragments in the wrapping region.

In some example embodiments, to calculate a density ratio, in Step a, acombined area of the text fragments within a wrapping region isidentified and/or calculated. In turn, in Step B, the area of all thetext fragment intersections is subtracted from the combined area of thetext fragments within the wrapping region identified in Step A. In StepC, the area identified in Step A is subtracted from the total area ofthe wrapping region's bounding box to obtain a value. And, in turn, atStep D, the value obtained in Step C is divided by the total area of thewrapping region's bounding box.

In some example embodiments, to calculate an alignment ratio, a list ofleft aligned groupings of text fragments within the wrapping region isidentified and/or generated. In turn, a list of right aligned groupingsof text fragments within the wrapping region is identified and/orgenerated. In turn, a list of center aligned groupings of text fragmentswithin the wrapping region are identified and/or generated. Thegroupings of left, right and center aligned text fragments areaggregated and/or combined into a single list that does not includeduplicates. In turn, the count of groupings in the list of all groupings(e.g., left, right, center) is divided by the number of fragment runs inthe wrapping region.

In some example embodiments, to calculate a capital or non-alphabeticratio, the starting character (e.g., first, lowest index, leftmost) ofthe starting (e.g., first, lowest index, leftmost) text fragment of eachfragment run of the wrapping region is identified and/or analyzed tocalculate a count of how many of those starting text characters are acapital letter or otherwise any Unicode character that is not alowercase letter. In turn, the calculated count is divided by the numberfragment runs in the wrapping region.

In some example embodiments, to calculate a bold text fragment count,the number of text fragments in the wrapping region whose font weighttext property (e.g., the whole text fragment or at least a portion ofthe text fragment) is higher than a predetermined normal font weight.

The calculated metrics are in turn used to calculate sub-scores.

In some example embodiments, one or more tabular score sub-scores arecalculated, including (1) a normalization ratio sub-score, (2) a densityratio sub-score, (3) an alignment ratio sub-score, and (4) a capital ornon-alphabetic ratio sub-score.

In some example embodiments, to calculate a tabular score normalizationratio sub-score, the normalization ratio is calculated (e.g., using theprocess described above). In turn, the normalization ratio is multipliedby a predetermined weight (e.g., 4) to produce the tabular scorenormalization ratio sub-score. A high degree of normalized fragments inthe wrapping region has a negative impact on its tabular score.

In some example embodiments, to calculate a tabular score density ratiosub-score, the density ratio is calculated (e.g., using the processdescribed above). In turn, the density ratio is subtracted from apredetermined value (e.g., 1) and that result is multiplied by apredetermined weight (e.g., 2) to produce the tabular score densityratio sub-score. A high fragment density value in the wrapping regionhas a negative impact on its tabular score.

In some example embodiments, to calculate a tabular score alignmentratio sub-score, the alignment ratio is calculated (e.g., using theprocess described above). In turn, the alignment ratio is multiplied bya predetermined weight (e.g., 2) to produce the tabular score alignmentratio sub-score. A high degree of aligned (e.g., right, left, center)text fragments in a wrapping region has a positive impact on its tabularscore.

In some example embodiments, to calculate a tabular score capital ornon-alphabetic ratio sub-score, the capital or non-alphabetic ratio iscalculated (e.g., using the process described above). In turn, thecapital or non-alphabetic ratio is multiplied by a predetermined weight(e.g., 2) to produce the tabular score capital or non-alphabetic ratiosub-score. A high degree of normalized text fragments that begin witheither a capital letter or non-alphabetic character in a wrapping regionhas a positive impact on its tabular score.

In some example embodiments, one or more narrative score sub-scores arecalculated, including (1) a normalization ratio sub-score, (2) a densityratio sub-score, (3) an alignment ratio sub-score, and (4) a capital ornon-alphabetic ratio sub-score.

In some example embodiments, to calculate a narrative scorenormalization ratio sub-score, the normalization ratio is calculated(e.g., using the process described above). In turn, the normalizationratio is subtracted from a predetermined value (e.g., 1) and multipliedby a predetermined weight (e.g., 4) to produce the narrative scorenormalization ratio sub-score. A high degree of normalized fragments inthe wrapping region has a positive impact on its narrative score.

In some example embodiments, to calculate a narrative score densityratio sub-score, the density ratio is calculated (e.g., using theprocess described above). In turn, the density ratio is multiplied by apredetermined weight (e.g., 2) to produce the narrative score densityratio sub-score. A high fragment density value in the wrapping regionhas a positive impact on its narrative score.

In some example embodiments, to calculate a narrative score alignmentratio sub-score, the alignment ratio is calculated (e.g., using theprocess described above). In turn, the alignment ratio is subtractedfrom a predetermined value (e.g., 1) and multiplied by a predeterminedweight (e.g., 2) to produce the narrative score alignment ratiosub-score. A high degree of aligned text fragments in the wrappingregion has a negative impact on its narrative score.

In some example embodiments, to calculate a narrative score capital ornon-alphabetic ratio sub-score, the capital or non-alphabetic ratio iscalculated (e.g., using the process described above). In turn, thecapital or non-alphabetic ratio is subtracted from a predetermined value(e.g., 1) and multiplied by a predetermined weight (e.g., 2) to producethe narrative score capital or non-alphabetic ratio sub-score. A highdegree of normalized text fragments that begin with either a capitalletter or non-alphabetic character in the wrapping region has a negativeimpact on its narrative score.

In some example embodiments, one or more label score sub-scores arecalculated, including (1) a normalization ratio sub-score, (2) a densityratio sub-score, (3) an text fragment quantity sub-score, and (4) a boldfragment count sub-score.

In some example embodiments, to calculate a label score normalizationratio sub-score, the normalization ratio is calculated (e.g., using theprocess described above). In turn, the normalization ratio is subtractedfrom a predetermined value (e.g., 1) and multiplied by a predeterminedweight (e.g., 4) to produce the label score normalization ratiosub-score. A high degree of normalized fragments in the wrapping regionhas a positive impact on its label score.

In some example embodiments, to calculate a label score density ratiosub-score, the density ratio is calculated (e.g., using the processdescribed above). In turn, the density ratio is multiplied by apredetermined weight (e.g., 2) to produce the label score density ratiosub-score. A high fragment density value in the wrapping region has apositive impact on its label score.

In some example embodiments, to calculate a label score text fragmentquantity sub-score, the text fragment quantity is calculated by countingand/or identifying the number of text fragments in the wrapping region.A fragment quantity cutoff (e.g., predetermined maximum number (e.g.,10)) is set. If the text fragment count or quantity is less than thefragment quantity cutoff, an intermediary score value is set to apredetermined value (e.g., 0.99). Otherwise, if the text fragment countor quantity is not less than the fragment quantity cutoff, theintermediary score value is set, for example to:(1−(0.05*(text_fragement_count−fragment_quantity_cutoff))). The higherof 0.0 and the intermediary score is selected and multiplied by apredetermined weight (e.g., 4.0) to produce the label score textfragment quantity sub-score. A high number (e.g., quantity, count) oftext fragments in the wrapping region has a negative impact on its labelscore.

In some example embodiments, to calculate a label score bold fragmentcount sub-score, a bold fragment count is calculated (e.g., using theprocess described above). In turn, the bold fragment count is analyzedto determine whether it is equal to 0.0 and, if so, the label score boldfragment count sub-score is not calculated. Otherwise, an intermediaryvalue is calculated using the formula(1−(text_fragment_count−bold_fragment_count)*0.05). The higher of apredetermine value (e.g., 0.5) and the intermediary value is selectedand multiplied by a predetermined weight (e.g., 6.0) to produce a labelscore bold_fragment_count sub-score. A high number of bold textfragments in the wrapping region has a positive impact on its labelscore.

It should be understood that the above values used to calculate themetrics and/or sub-scores are exemplary embodiments, and other valuesmay be used in accordance with the present algorithms.

In some example embodiments, the sub-scores are calculated for all or aportion of the wrapping regions prior to calculating the type scores.The type scores are used to determine and/or identify how closely awrapping region identifies as a tabular, narrative or label block typewrapping region. In some example embodiments, type scores (e.g., scores)are decimal values between (e.g., between 0.0 and 1.0) that are producedby calculating and combining the sub-scores. That is, the tabularsub-scores are combined with each other, the narrative sub-scores arecombined with each other, and the label sub-scores are combined witheach other, to generate the respective type scores. To combinesub-scores, a sum of the sub-scores to be combined is divided by the sumof the weights used to calculate those sub-scores (e.g., the weightedaverage of the sub-scores corresponding to the wrapping region).

As a result, type scores (e.g., tabular score, narrative score, andlabel score) for each wrapping region are calculated. That is, the typescores are calculated using the calculated weighted average of thesub-scores corresponding to the wrapping regions, thereby identifyinghow closely each wrapping region is to a tabular, narrative or labelblock type.

At step 454 of FIG. 4B, the block types (e.g., tabular, narrative,label) are assigned to each wrapping region in the wrapping regioncollection. In some example embodiments, the block type that is assignedto a wrapping region is based on the highest of the type scores computedat step 452 for that wrapping region. Thus, if the label score is better(e.g., higher) than the narrative and tabular scores for a wrappingregion, the wrapping region is labeled, marked and/or identified as alabel-type wrapping region.

In some example embodiments, special conditions are applied whenassigning block types to each wrapping region. For example, one specialcondition is that a wrapping region made up of a single numeric fragmentis, in some example embodiments, is given a 1.0 tabular score. Anotherspecial condition may be that a wrapping region made up of a single textfragment is always given a predetermined (e.g., 1.0) label score.

It should be understood that in some example embodiments, the scores,ratios, weights, and ranges described above in connection with FIG. 4Bcan be modified to fit a different model.

Aggregation Algorithms

FIG. 5A illustrates a flow chart 500A for executing a horizontalaggregation algorithm or a horizontal aggregation portion of anaggregation algorithm, according to an exemplary embodiment. As shown inFIG. 5A, at step 520, wrapping regions associated with a rendered pageof a print-ready digital source document are acquired, retrieved and/oridentified. In some example embodiments, the wrapping regions areassociated with spatial coordinates on the rendered page and their blocktypes have been determined, using, for instance, the wrapping andclassification algorithms described above in connection with FIGS. 2Gand 4B, respectively.

FIG. 5C illustrates an interface 500C for generating tables fromprint-ready digital source documents according to an exemplaryembodiment. In FIG. 5C, wrapping regions that have been acquired,retrieved and/or identified are delineated by corresponding boundingboxes. For instance, FIG. 5C illustrates wrapping regions such aswrapping region 501 and 503, each of which is associated with and/or hasspatial coordinates on the rendered page, as well as a correspondingblock type (e.g., tabular data).

In turn, at step 522, an empty wrapping region group set (or wrappingregion group collection) is created, generated and/or retrieved. At step524, for each wrapping region acquired at step 520, a correspondingempty wrapping region group is created, and each wrapping region isadded to its corresponding wrapping region group. In this way, eachwrapping region acquired at step 520 is added to its own correspondingwrapping region group which contains no other data.

At step 526, the wrapping region groups are added to a coordinate map ofthe rendered page, based on the spatial coordinates of the wrappingregion groups. The wrapping region groups are indexed according to theirrespective spatial coordinates, for example, ordered by their appearanceon the rendered page, from top down and left to right, such that atopmost and leftmost wrapping region group is the wrapping region groupwith the first index on the rendered page and the wrapping region groupto its right is the wrapping region group with the second index on therendered page. In this way, the wrapping region group with the lastindex on the rendered page is the bottommost and rightmost wrappingregion group on the rendered page.

At step 528, the tallest, uppermost and leftmost wrapping region groupon the coordinate map that is of a tabular block type is located. Adetermination is made at step 530 as to whether a tallest, uppermost andleftmost wrapping region group on the coordinate map that is of atabular block type was identified at step 528. That is, thedetermination at step 530 identifies whether any tabular wrapping regiongroups remain to be processed. If it is determined at step 530 that nosuch wrapping region groups were located and/or identified at step 528,the aggregation algorithm concludes and/or determines, at step 532, thatthe wrapping region group set is complete.

On the other hand, if it is determined at step 530 that a tallest,uppermost and leftmost wrapping region group on the coordinate map thatis of a tabular block type was identified and/or located at step 528,that wrapping region group (e.g., the wrapping region group identifiedat step 528) is labeled, assigned, marked and/or flagged as the currentwrapping region group at step 534.

In turn, at step 536, a rectangle is created, matching the dimensionsand spatial coordinates or position of the bounding box (e.g.,rectangular area) of the current wrapping region group. At step 538, theleft and right edges, borders and/or boundaries of the rectangle createdat step 534 are extended to match the right and left edges, bordersand/or boundaries of the bounding box of the coordinate map. FIG. 5Dillustrates an interface 500D for generating tables from print-readydigital source documents according to an exemplary embodiment. As shownin FIG. 5D, rectangle 505 has been created and its right and left edgeshave been extended to match the right and left edges of the coordinatemap. In FIG. 5D, the rectangle 505 was created to match the dimensionsand spatial position of the bounding box of wrapping region 501, whichis the tallest, uppermost and leftmost wrapping region group that is ofa tabular type.

At step 540, using the coordinate map, wrapping region groups whosebounding boxes intersect with the rectangle (e.g., rectangle 505)created at step 536 are identified and added to a list (e.g.,intersecting wrapping region groups list). That is, at step 540, eachwrapping region group on the coordinate map is analyzed to determinewhether its bounding box at all intersects with the extended rectangleof the current wrapping region group. At step 542, the list ofintersecting wrapping region groups is analyzed to determine whether anywrapping region groups remain in it (e.g., to determine if anyintersecting wrapping region groups still need to be processed). If itis determined at step 542 that no intersecting wrapping region groupsremain in the list, the aggregation algorithm proceeds to step 552,which is described in further detail below.

On the other hand, if it is determined at step 542 that intersectingwrapping region groups indeed remain in the list (e.g., the list createdat step 540), the intersecting wrapping region group that is at the topof the list (e.g., first index) is removed from the list at step 544.The wrapping region group removed from the list at step 544 is analyzedand a corresponding merge score is computed for that wrapping regiongroup at step 546.

The merge score calculated and/or computed at step 546 is used todetermine if two wrapping region groups should be merged (e.g., becausethe wrapping region groups have similar and/or matchingcharacteristics). For example, at step 546, the merge score for thewrapping region group removed from the list at step 544 indicateswhether it should be merged with the current wrapping region group. Themerge score is calculated from sub-scores based on properties of all ora portion of the wrapping region groups. In some example embodiments,the sub-scores are decimal values (e.g., between 0.0 and 1.0) thatpossess an integer weight. The merge score is calculated by combiningmerge score sub-scores. To combine the merge score sub-scores, thesub-scores are summed and divided by the sum of the weights used tocalculate and/or corresponding to the sub-scores.

For example, sub-scores (e.g., merge score sub-scores) corresponding tothe wrapping region groups are based on and/or calculated on thefollowing properties:

-   -   Vertical alignment: Top and bottom alignment between two        wrapping region groups has a positive impact on their merge        score.    -   Block type: Matching block types between two wrapping region        groups has a positive impact on their merge score.    -   Matching lines: Lines that match on aspects such as vertical        position and bounding box height have a positive impact on the        wrapping regions' merge score.

In some example embodiments, to calculate a merge score verticalalignment sub-score, the sub-score is initialized to a predeterminedvalue (e.g., 0.0). The top (e.g., based on measurements or verticalcoordinates of the top boundary on the rendered page) of a firstwrapping region and the top (e.g., based on measurements or coordinatesof the top boundary on the rendered page) of a second wrapping region tobe potentially merged are compared. The absolute value of the differencebetween the top of the first wrapping region and the top of the secondwrapping region is calculated. If the calculated absolute value iswithin a predetermined tolerance (e.g., 10 units), the first and secondwrapping regions are considered to be top aligned with one another. Inturn, the bottom (e.g., based on measurements or vertical coordinates ofthe bottom boundary on the rendered page) of the first wrapping regionand the bottom (e.g., based on measurements or vertical coordinates ofthe bottom boundary on the rendered page) of the second wrapping regionare compared. The absolute value of the difference between the bottom ofthe first wrapping region and the bottom of the second wrapping regionis calculated. If the calculated absolute value is within apredetermined tolerance (e.g., 10 units), the first and second wrappingregions are considered to be bottom aligned. A match percentage betweenthe first and second wrapping regions is added to the correspondingsub-score. For example, if there is no match (e.g., based on thevertical alignment) between the wrapping regions, the match percentageadded to the sub-score is 0.0; if there is a partial match (e.g., somevertical alignment), the match percentage added to the sub-score isbetween 0.0 and 1.0; and, if there is a perfect match (e.g., fullvertical alignment), the match percentage added to the sub-score is 1.0.In turn, the sub-score is multiplied by a predetermined weight (e.g.,3.0) to produce the merge score vertical alignment sub-score.

In some example embodiments, to calculate a merge score block typesub-score, the sub-score is initialized to a predetermined value (e.g.,0.0). If the type (e.g., narrative, tabular, label) of a first wrappingregion is equal to or the same as the type of a second wrapping region,1.0 is added to the sub-score. In turn, the sub-score is multiplied by apredetermined weight (e.g., 3.0) to produce the merge score block typesub-score

In some example embodiments, to calculate a merge score matching linessub-score, the sub-score is initialized to a predetermined value (e.g.,0.0). Among two wrapping regions being considered to be merged, it isdetermined which of the two wrapping regions has fewer lines. Thewrapping region of the two wrapping regions that has the fewest numberof lines is assigned and/or labeled as the first wrapping region, andthe other wrapping region is assigned and/or labeled as the secondwrapping region. For each line the first wrapping region, it isdetermined whether there is a matching line in the second wrappingregion. In some example embodiments, matching lines are two lines, indifferent wrapping regions, that have the same vertical alignment as oneanother. A value is calculated by taking the number of matching linesbetween the first and second wrapping regions and dividing it by theabsolute value of the difference in the number of lines in the firstwrapping region and the number of lines in the second wrapping region(e.g., matching_lines/(|lines_in_first_wrappingregion−lines_in_second_wrapping_region|)). The calculated value is addedto the sub-score and the sub-score is multiplied by a predeterminedweight (e.g., 3.0) to produce the merge score matching lines sub-score.

In turn, at step 548, the merge score calculated at step 546 for thewrapping region group removed from the list at step 544 is analyzed todetermine whether it is equal to or greater than a predetermined mergethreshold. If it is determined at step 548 that the merge score is notequal to or greater than the merge threshold, the wrapping region groupremoved from the list at step 544 is not merged and the aggregationalgorithm returns to step 542. Steps 542, 544, 546 and 548 are repeatedwith the next intersecting wrapping region group at the top of the list,until no intersecting wrapping region groups remain in the list, atwhich point the aggregation algorithm proceeds to step 552, which isdescribed in further detail below.

If, on the other hand, it is determined at step 548 that the merge scorefor the wrapping region group removed from the list is indeed equal toor greater than the merge threshold, the wrapping region group removedfrom the list is merged into or with the current wrapping region group.In turn, the aggregation algorithm returns to step 542. Steps 542, 544,546 and 548 are repeated with the next intersecting wrapping regiongroup at the top of the list, until no intersecting wrapping regiongroups remain in the list, at which point the aggregation algorithmproceeds to step 552, which is described in further detail below.

At step 552, wrapping region groups that have been merged with and/orinto the current wrapping region group are removed from the coordinatemap. At step 554, the current wrapping region group is added and/orappended to the wrapping region group set created at step 522. In turn,the aggregation algorithm returns to step 528, where the tallest,uppermost and leftmost wrapping region group on the coordinate map thatis of a tabular block type is located. It should be understood that thecoordinate map, on each subsequent iteration, has had wrapping regiongroups removed from it and therefore the tallest, uppermost and leftmostwrapping region group on the coordinate map that is of a tabular blocktype that is located on subsequent iterations is different thanpreviously identified tallest, uppermost and leftmost wrapping regiongroups on the coordinate map that are of a tabular block type.

The horizontal aggregation algorithm described above in connection withFIG. 5A results in wrapping regions being merged. FIG. 5E illustrates aninterface 500E for generating tables from print-ready digital sourcedocuments according to an exemplary embodiment. As shown in FIG. 5E,wrapping regions identified in FIG. 5D have been merged into wrappingregion groups such as wrapping region group 507. A merge of wrappingregions indicates, in some instances, that merged wrapping regions are,correspond to, and/or should be considered as part of a same table.

FIG. 5B illustrates a flow chart 500B for executing a verticalaggregation algorithm or a vertical aggregation portion of anaggregation algorithm, according to an exemplary embodiment.

As shown in FIG. 5B, at step 556, a wrapping region group set includingwrapping region groups associated with a rendered page of a print-readydigital source document are acquired, retrieved and/or identified. Insome example embodiments, the wrapping region groups are associated withspatial coordinates on the rendered page and their block types have beendetermined, using, for instance, the wrapping and classificationalgorithms described above in connection with FIGS. 2G and 4B,respectively. The wrapping region groups, in some example embodiments,are acquired, retrieved and/or identified using the horizontalaggregation algorithm described above in connection with FIG. 5A.

In turn, at step 558, an empty block set (or block table collection) iscreated, generated and/or retrieved. At step 560, for each wrappingregion group in the wrapping group set acquired at step 556, acorresponding empty block is created, and each wrapping region group isadded to its corresponding block. In this way, each wrapping regiongroup acquired at step 556 is added to its own corresponding block whichcontains no other data.

At step 562, the blocks are added to a coordinate map of the renderedpage, based on the spatial coordinates of the blocks. The blocks areindexed according to their respective spatial coordinates, for example,ordered by their appearance on the rendered page, from top down and leftto right, such that a topmost and leftmost block is the block with thefirst index on the rendered page and the block to its right is the blockwith the second index on the rendered page. In this way, the block withthe last index on the rendered page is the bottommost and rightmostblock on the rendered page.

At step 564, the widest, uppermost and leftmost block on the coordinatemap that is of a tabular block type is located. The widest, uppermostand leftmost block in that priority order (e.g.,widest>uppermost>leftmost). A determination is made at step 566 as towhether a widest, uppermost and leftmost block on the coordinate mapthat is of a tabular block type was identified or located at step 564.That is, the determination at step 566 identifies whether any tabularblocks remain to be processed. If it is determined at step 566 that nosuch blocks were located and/or identified at step 564, the aggregationalgorithm concludes and/or determines, at step 568, that the block setis complete.

On the other hand, if it is determined at step 566 that a widest,uppermost and leftmost block on the coordinate map that is of a tabularblock type was identified and/or located at step 564, that block (e.g.,the block identified at step 564) is labeled, assigned, marked and/orflagged as the current block at step 570.

In turn, at step 572, a rectangle is created, matching the dimensionsand spatial coordinates or position of the bounding box (e.g.,rectangular area) of the current block. At step 574, the top and bottomedges, borders and/or boundaries of the rectangle created at step 572are extended to match the top and bottom edges, borders and/orboundaries of the bounding box of the coordinate map. FIG. 5Fillustrates an interface 500F for generating tables from print-readydigital source documents according to an exemplary embodiment. As shownin FIG. 5F, rectangle 509 has been created and its top and bottom edgeshave been extended to match the right and left edges of the coordinatemap. In FIG. 5F, the rectangle 509 was created to match the dimensionsand spatial position of the bounding box of wrapping region group whichis the widest, uppermost and leftmost block on the coordinate map thatis of a tabular block type.

At step 576, using the coordinate map, blocks whose bounding boxesintersect with the rectangle (e.g., rectangle 509) created at step 572and extended at step 574 are identified and added to a list (e.g.,intersecting blocks list). That is, at step 576, each block on thecoordinate map is analyzed to determine whether its bounding box at allintersects with the extended rectangle of the current block. At step578, the list of intersecting blocks is analyzed to determine whetherany blocks remain in it (e.g., to determine if any intersecting blocksstill need to be processed). If it is determined at step 578 that nointersecting blocks remain in the list, the aggregation algorithmproceeds to step 588, which is described in further detail below.

On the other hand, if it is determined at step 578 that intersectingblocks indeed remain in the list (e.g., the list created at step 576),the intersecting block that is at the top of the list (e.g., firstindex) is removed from the list at step 580. The block removed from thelist at step 580 is analyzed and a corresponding merge score is computedfor that block at step 582.

The merge score calculated and/or computed at step 582 is used todetermine if two blocks should be merged (e.g., because the blocks havesimilar and/or matching characteristics). For example, at step 582, themerge score for the block removed from the list at step 580 indicateswhether it should be merged with the current blocks. The merge score iscalculated from sub-scores based on properties of all or a portion ofthe blocks. In some example embodiments, the sub-scores are decimalvalues (e.g., between 0.0 and 1.0) that possess an integer weight. Themerge score is calculated by combining merge score sub-scores. Thecombine the merge score sub-scores, the sub-scores are summed anddivided by the sum of the weights used to calculate and/or correspondingto the sub-scores.

For example, properties calculated and/or used to produce sub-scores(e.g., merge score sub-scores) corresponding to the blocks include:

-   -   Horizontal alignment: Left and right alignment between two        blocks has a positive impact on their merge score.    -   Column position: Horizontal overlap between the columns of two        blocks has a positive impact on their merge score.    -   Column alignment: Columns with matching position as well as        matching wrapping region alignment (left, right, center) have a        positive impact on the merge score of the blocks.    -   Column data type: Columns with matching position as well as        matching data type have a positive impact on the merge score of        the blocks.

In some example embodiments, to calculate a merge score horizontalalignment subs-score, the sub-score is initialized to a predeterminedvalue (e.g., 0.0). The left side (e.g., based on measurements orhorizontal coordinates of the left boundary on the rendered page) of afirst block and the left side (e.g., based on measurements or horizontalcoordinates of the left boundary on the rendered page) of a second blockare compared. The absolute value of the difference between the left ofthe first block and the left of the second block is calculated. If thecalculated absolute value is within a predetermined tolerance (e.g., 10units), the first and second blocks are considered to be left aligned.In turn, the right side (e.g., based on measurements or horizontalcoordinates of the right boundary on the rendered page) of the firstblock and the right side (e.g., based on measurements or horizontalcoordinates of the right boundary on the rendered page) of the secondblock are compared. The absolute value of the difference between theright of the first block and the right of the second block iscalculated. If the calculated absolute value is within a predeterminedtolerance (e.g., 10 units), the first and second blocks are consideredto be right aligned. A match percentage between the first and secondblocks is added to the corresponding sub-score. For example, if there isno match (e.g., based on the horizontal alignment) between the blocks,the match percentage added to the sub-score is 0.0; if there is apartial match (e.g., some horizontal alignment), the match percentageadded to the sub-score is between 0.0 and 1.0; and, if there is aperfect match (e.g., full horizontal alignment), the match percentageadded to the sub-score is 1.0. In turn, the sub-score is multiplied by apredetermined weight (e.g., 3.0) to produce the merge score horizontalalignment sub-score.

In some example embodiments, to calculate a merge score column positionsub-score, the sub-score is initialized to a predetermined value (e.g.,0.0). Among two blocks being considered to be merged, it is determinedwhich of the two blocks has the fewest number of columns. The block ofthe two blocks that has the fewest number of columns is assigned and/orlabeled as the first block, and the other block is assigned and/orlabeled as the second block. For each column in the first block, it isdetermined whether there (1) is a column in the second block that hasmatching and/or similar horizontal alignment as the column in the firstblock, and/or (2) are columns in the second block that have horizontaloverlap with the column in the first block. If a column in the firstblock has a perfect horizontal alignment to a column in the secondblock, it is considered to be “strongly positioned.” Each column in thesecond block that is determined to be “strongly positioned” causes thesub-score to be incremented by a predetermined amount (e.g., 0.1). If acolumn in the first block has a partial horizontal alignment to a columnin the second block, it is determined to be “weakly positioned.” Eachcolumn in the second block that is determined to be “weakly positioned”causes the sub-score to be incremented by a lesser predetermined amount(e.g., 0.05) than for “strongly positioned” columns. In some exampleembodiments, if a column from the first block is identified and/ordetermined to overlap multiple columns in the second block, a sub-scoreof 0.0 is automatically returned. In turn, the sub-score is multipliedby a predetermined weight (e.g., 3.) to produce the merge score columnposition sub-score.

In some example embodiments, to calculate a merge score column alignmentsub-score, the sub-score is initialized to a predetermined value (e.g.,0.0). Among two blocks being considered to be merged, it is determinedwhich of the two blocks has the fewest number of columns. The block ofthe two blocks that has the fewest number of columns is assigned and/orlabeled as the first block, and the other block is assigned and/orlabeled as the second block. For each column in the first block, it isdetermined whether there is a column in the second block that has thesame left and/or right alignment (e.g., based on measurements orhorizontal coordinates of the left and right boundaries on the renderedpage). The number of matching columns divided by the absolute value ofthe difference in the number of columns in the first block and thenumber of columns in the second block are added to the sub score (e.g.,matching columns/(|columns_in_first_block−columns_in_second_block|)).The sub-score is multiplied by a predetermined weight (e.g., 3.0) toproduce the merge score column alignment sub-score.

In some example embodiments, to calculate a merge score column data typesub-score, the sub-score is initialized to a predetermined value (e.g.,0.0). Among two blocks being considered to be merged, it is determinedwhich of the two blocks has the fewest number of columns. The block ofthe two blocks that has the fewest number of columns is assigned and/orlabeled as the first block, and the other block is assigned and/orlabeled as the second block. For each column in the first block, it isdetermined whether there is a column in the second block that has thesame left and/or right alignment (e.g., based on measurements orhorizontal coordinates of the left and right boundaries on the renderedpage). If such a column is identified the data type (e.g., tabular,label, narrative) of the column in the first block is compared to thedata type of the column in the second block. If the data types of thetwo columns match and/or are equal to each other, the sub-score isincremented by: 1/columns_in_first_block (e.g.,sub-score=sub-score+(1/columns_in_first_block)). In turn, the sub-scoreis multiplied by a predetermined weight (e.g., 3.0) to produce the mergescore column data type sub-score.

In turn, at step 584, the merge score calculated at step 582 for theblock removed from the list at step 580 is analyzed to determine whetherit is equal to or greater than a predetermined merge threshold. If it isdetermined at step 584 that the merge score is not equal to or greaterthan the merge threshold, the block removed from the list at step 580 isnot merged and the aggregation algorithm returns to step 578. Steps 578,580, 582 and 584 are repeated with the next intersecting block at thetop of the list, until no intersecting blocks remain in the list, atwhich point the aggregation algorithm proceeds to step 588, which isdescribed in further detail below.

If, on the other hand, it is determined at step 584 that the merge scorefor the block removed from the list is indeed equal to or greater thanthe merge threshold, the block removed from the list is merged into orwith the current block. In turn, the aggregation algorithm returns tostep 578. Steps 578, 580, 582 and 584 are repeated with the nextintersecting block at the top of the list, until no intersecting blocksremain in the list, at which point the aggregation algorithm proceeds tostep 588, which is described in further detail below.

At step 588, blocks that have been merged with and/or into the currentblock are removed from the coordinate map. At step 590, the currentblock is added and/or appended to the block set created at step 558. Inturn, the aggregation algorithm returns to step 564, where the widest,uppermost and leftmost block on the coordinate map that is of a tabularblock type is located. It should be understood that the coordinate map,on each subsequent iteration, has had blocks removed from it andtherefore widest, uppermost and leftmost block on the coordinate mapthat is of a tabular block type that is located on subsequent iterationsis different than previously identified widest, uppermost and leftmostblocks on the coordinate map that are of a tabular block type islocated.

The vertical aggregation algorithm described above in connection withFIG. 5B results in blocks being merged into a block set. FIG. 5Gillustrates an interface 500G for generating tables from print-readydigital source documents according to an exemplary embodiment. As shownin FIG. 5G, blocks are merged into a block set such as block set 511. Amerge of blocks indicates, in some instances, that merged blocks are,correspond to, and/or should be considered as part of a same table.

A table is generated from and/or corresponds to the block set, such thateach text fragment in a block set corresponds to a field, cell or thelike (e.g., row, column intersection). For example, Table 1 belowillustrates a portion of a table generated from the block set 511identified using the aggregation algorithm described in connection withFIGS. 5A and 5B:

TABLE 1 Actual- Percent $ Millions Mar-14 Mar-15 Mar-15E EstimateDifference Software 4.4 3.9 3.9 0.3 9% Licenses Maintenance 3.1 3.3 3.3(0.0) 0% Professional 0.5 0.3 0.3 (0.1) −36% Services Total Revenues 8.07.5 7.5 0.2 2%

FIG. 6 shows an illustrative network environment 600 for use in themethods and systems described herein. In brief overview, referring nowto FIG. 6, a block diagram of an exemplary cloud computing environment600 is shown and described. The cloud computing environment 600 mayinclude one or more resource providers 602 a, 602 b, 602 c(collectively, 602). Each resource provider 602 may include computingresources. In some implementations, computing resources may include anyhardware and/or software used to process data. For example, computingresources may include hardware and/or software capable of executingalgorithms, computer programs, and/or computer applications. In someimplementations, exemplary computing resources may include applicationservers and/or databases with storage and retrieval capabilities. Eachresource provider 602 may be connected to any other resource provider602 in the cloud computing environment 600. In some implementations, theresource providers 602 may be connected over a computer network 608.Each resource provider 602 may be connected to one or more computingdevice 604 a, 604 b, 604 c (collectively, 604), over the computernetwork 608.

The cloud computing environment 600 may include a resource manager 606.The resource manager 606 may be connected to the resource providers 602and the computing devices 604 over the computer network 608. In someimplementations, the resource manager 606 may facilitate the provisionof computing resources by one or more resource providers 602 to one ormore computing devices 604. The resource manager 606 may receive arequest for a computing resource from a particular computing device 604.The resource manager 606 may identify one or more resource providers 602capable of providing the computing resource requested by the computingdevice 604. The resource manager 606 may select a resource provider 602to provide the computing resource. The resource manager 606 mayfacilitate a connection between the resource provider 602 and aparticular computing device 604. In some implementations, the resourcemanager 606 may establish a connection between a particular resourceprovider 602 and a particular computing device 604. In someimplementations, the resource manager 606 may redirect a particularcomputing device 604 to a particular resource provider 602 with therequested computing resource.

FIG. 7 shows an example of a computing device 700 and a mobile computingdevice 750 that can be used in the methods and systems described in thisdisclosure. The computing device 700 is intended to represent variousforms of digital computers, such as laptops, desktops, workstations,personal digital assistants, servers, blade servers, mainframes, andother appropriate computers. The mobile computing device 750 is intendedto represent various forms of mobile devices, such as personal digitalassistants, cellular telephones, smart-phones, and other similarcomputing devices. The components shown here, their connections andrelationships, and their functions, are meant to be examples only, andare not meant to be limiting.

The computing device 700 includes a processor 702, a memory 704, astorage device 706, a high-speed interface 708 connecting to the memory704 and multiple high-speed expansion ports 710, and a low-speedinterface 712 connecting to a low-speed expansion port 714 and thestorage device 706. Each of the processor 702, the memory 704, thestorage device 706, the high-speed interface 708, the high-speedexpansion ports 710, and the low-speed interface 712, are interconnectedusing various busses, and may be mounted on a common motherboard or inother manners as appropriate. The processor 702 can process instructionsfor execution within the computing device 700, including instructionsstored in the memory 704 or on the storage device 706 to displaygraphical information for a GUI on an external input/output device, suchas a display 716 coupled to the high-speed interface 708. In otherimplementations, multiple processors and/or multiple buses may be used,as appropriate, along with multiple memories and types of memory. Also,multiple computing devices may be connected, with each device providingportions of the necessary operations (e.g., as a server bank, a group ofblade servers, or a multi-processor system).

The memory 704 stores information within the computing device 700. Insome implementations, the memory 704 is a volatile memory unit or units.In some implementations, the memory 704 is a non-volatile memory unit orunits. The memory 704 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 706 is capable of providing mass storage for thecomputing device 700. In some implementations, the storage device 706may be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. Instructions can be stored in an information carrier.The instructions, when executed by one or more processing devices (forexample, processor 702), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices such as computer- or machine-readable mediums (forexample, the memory 704, the storage device 706, or memory on theprocessor 702).

The high-speed interface 708 manages bandwidth-intensive operations forthe computing device 700, while the low-speed interface 712 manageslower bandwidth-intensive operations. Such allocation of functions is anexample only. In some implementations, the high-speed interface 708 iscoupled to the memory 704, the display 716 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 710,which may accept various expansion cards (not shown). In theimplementation, the low-speed interface 712 is coupled to the storagedevice 706 and the low-speed expansion port 714. The low-speed expansionport 714, which may include various communication ports (e.g., USB,Bluetooth®, Ethernet, wireless Ethernet) may be coupled to one or moreinput/output devices, such as a keyboard, a pointing device, a scanner,or a networking device such as a switch or router, e.g., through anetwork adapter.

The computing device 700 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 720, or multiple times in a group of such servers. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 722. It may also be implemented as part of a rack server system724. Alternatively, components from the computing device 700 may becombined with other components in a mobile device (not shown), such as amobile computing device 750. Each of such devices may contain one ormore of the computing device 700 and the mobile computing device 750,and an entire system may be made up of multiple computing devicescommunicating with each other.

The mobile computing device 750 includes a processor 752, a memory 764,an input/output device such as a display 754, a communication interface766, and a transceiver 768, among other components. The mobile computingdevice 750 may also be provided with a storage device, such as amicro-drive or other device, to provide additional storage. Each of theprocessor 752, the memory 764, the display 754, the communicationinterface 766, and the transceiver 768, are interconnected using variousbuses, and several of the components may be mounted on a commonmotherboard or in other manners as appropriate.

The processor 752 can execute instructions within the mobile computingdevice 750, including instructions stored in the memory 764. Theprocessor 752 may be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 752may provide, for example, for coordination of the other components ofthe mobile computing device 750, such as control of user interfaces,applications run by the mobile computing device 750, and wirelesscommunication by the mobile computing device 750.

The processor 752 may communicate with a user through a controlinterface 758 and a display interface 756 coupled to the display 754.The display 754 may be, for example, a TFT (Thin-Film-Transistor LiquidCrystal Display) display or an OLED (Organic Light Emitting Diode)display, or other appropriate display technology. The display interface756 may comprise appropriate circuitry for driving the display 754 topresent graphical and other information to a user. The control interface758 may receive commands from a user and convert them for submission tothe processor 752. In addition, an external interface 762 may providecommunication with the processor 752, so as to enable near areacommunication of the mobile computing device 750 with other devices. Theexternal interface 762 may provide, for example, for wired communicationin some implementations, or for wireless communication in otherimplementations, and multiple interfaces may also be used.

The memory 764 stores information within the mobile computing device750. The memory 764 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 774 may also beprovided and connected to the mobile computing device 750 through anexpansion interface 772, which may include, for example, a SIMM (SingleIn Line Memory Module) card interface. The expansion memory 774 mayprovide extra storage space for the mobile computing device 750, or mayalso store applications or other information for the mobile computingdevice 750. Specifically, the expansion memory 774 may includeinstructions to carry out or supplement the processes described above,and may include secure information also. Thus, for example, theexpansion memory 774 may be provided as a security module for the mobilecomputing device 750, and may be programmed with instructions thatpermit secure use of the mobile computing device 750. In addition,secure applications may be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory(non-volatile random access memory), as discussed below. In someimplementations, instructions are stored in an information carrier and,when executed by one or more processing devices (for example, processor752), perform one or more methods, such as those described above. Theinstructions can also be stored by one or more storage devices, such asone or more computer- or machine-readable mediums (for example, thememory 764, the expansion memory 774, or memory on the processor 752).In some implementations, the instructions can be received in apropagated signal, for example, over the transceiver 768 or the externalinterface 762.

The mobile computing device 750 may communicate wirelessly through thecommunication interface 766, which may include digital signal processingcircuitry where necessary. The communication interface 766 may providefor communications under various modes or protocols, such as GSM voicecalls (Global System for Mobile communications), SMS (Short MessageService), EMS (Enhanced Messaging Service), or MMS messaging (MultimediaMessaging Service), CDMA (code division multiple access), TDMA (timedivision multiple access), PDC (Personal Digital Cellular), WCDMA(Wideband Code Division Multiple Access), CDMA2000, or GPRS (GeneralPacket Radio Service), among others. Such communication may occur, forexample, through the transceiver 768 using a radio-frequency. Inaddition, short-range communication may occur, such as using aBluetooth®, Wi-Fi™, or other such transceiver (not shown). In addition,a GPS (Global Positioning System) receiver module 770 may provideadditional navigation- and location-related wireless data to the mobilecomputing device 750, which may be used as appropriate by applicationsrunning on the mobile computing device 750.

The mobile computing device 750 may also communicate audibly using anaudio codec 760, which may receive spoken information from a user andconvert it to usable digital information. The audio codec 760 maylikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 750. Such sound mayinclude sound from voice telephone calls, may include recorded sound(e.g., voice messages, music files, etc.) and may also include soundgenerated by applications operating on the mobile computing device 750.

The mobile computing device 750 may be implemented in a number ofdifferent forms, as shown in the figure. For example, it may beimplemented as a cellular telephone 780. It may also be implemented aspart of a smart-phone 782, personal digital assistant, or other similarmobile device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms machine-readable medium andcomputer-readable medium refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term machine-readable signal refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (LAN), a wide area network (WAN), and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

What is claimed is:
 1. A method comprising: receiving, by a computingsystem, a print-ready digital source document, the digital sourcedocument comprising at least one rendered page; generating, by thecomputing system, one or more wrapping regions based on the digitalsource document, wherein each wrapping region comprises one or morefragment runs, and wherein each fragment run comprises one or more textfragments of the digital source document that are adjacent to oneanother and within a horizontal separation threshold and a verticalseparation threshold; classifying, by the computing system, each of theone or more wrapping regions, wherein classifying each of the one ormore wrapping regions comprises calculating, for each of the one or morewrapping regions, a tabular score, a narrative score, and a label score,wherein each of the tabular score, the narrative score, and the labelscore is a measure of qualification of a wrapping region as a tabularblock type, as a narrative block type, and as a label block type,respectively; generating, by the computing system, one or more blocks ofwrapping regions based on the classifications of the one or morewrapping regions; generating, by the computing system, one or moretables based on one or more blocks of wrapping regions; and providing,by the computing system, an electronic document comprising the one ormore tables.
 2. The method of claim 1, wherein the print-ready digitalsource document is at least one of an XPS document, an RTF document, ora PDF document.
 3. The method of claim 1, wherein the print-readydigital source document comprises a fixed layout file.
 4. The method ofclaim 1, wherein at least one of the tabular score, the narrative scoreor the label score of each of the one or more wrapping regions arecalculated based on a normalization ratio, wherein the normalizationratio is a number of normalized text fragments divided by the totalnumber of text fragments within a wrapping region.
 5. The method ofclaim 1, wherein at least one of the tabular score, the narrative scoreor the label score of each of the one or more wrapping regions arecalculated based on a density ratio, wherein the density ratio is apercentage of a bounding box area of a wrapping region occupied by textfragment bounding boxes within the wrapping region.
 6. The method ofclaim 1, wherein at least one of the tabular score, the narrative scoreor the label score of each of the one or more wrapping regions arecalculated based on an alignment ratio, wherein the alignment ratio is anumber of normalized text fragments that fit within at least onealignment group divided by a total number of normalized text fragmentswithin a wrapping region.
 7. The method of claim 1, wherein at least oneof the tabular score, the narrative score or the label score of each ofthe one or more wrapping regions are calculated based on a capital ornon-alphabetic ratio, wherein the capital or non-alphabetic ratio is anumber of normalized text fragments that start with either a capitalletter or a non-alphabetic character divided by a number of normalizedtext fragments within a wrapping region.
 8. The method of claim 1,wherein at least one of the tabular score, the narrative score or thelabel score of each of the one or more wrapping regions are calculatedbased on a text fragment quantity, wherein the text fragment quantity isa number of text fragments within a wrapping region.
 9. The method ofclaim 1, wherein at least one of the tabular score, the narrative scoreor the label score of each of the one or more wrapping regions arecalculated based on a bold count, wherein the bold count is the numberof text fragments with bolded text within a wrapping region.
 10. Asystem comprising: one or more processors; one or more non-transitorycomputer-readable media including one or more sequences of instructionswhich, when executed by the one or more processors, causes: receiving,by a computing system, a print-ready digital source document, thedigital source document comprising at least one rendered page;generating, by the computing system, one or more wrapping regions basedon the digital source document, wherein each wrapping region comprisesone or more fragment runs, and wherein each fragment run comprises oneor more text fragments of the digital source document that are adjacentto one another and within a horizontal separation threshold and avertical separation threshold; classifying, by the computing system,each of the one or more wrapping regions, wherein classifying each ofthe one or more wrapping regions comprises calculating, for each of theone or more wrapping regions, a tabular score, a narrative score, and alabel score, wherein each of the tabular score, the narrative score, andthe label score is a measure of qualification of a wrapping region as atabular block type, as a narrative block type, and as a label blocktype, respectively; generating, by the computing system, one or moreblocks of wrapping regions based on the classifications of the one ormore wrapping regions; generating, by the computing system, one or moretables based on one or more blocks of wrapping regions; and providing,by the computing system, an electronic document comprising the one ormore tables.
 11. The system of claim 10, wherein the print-ready digitalsource document is at least one of an XPS document, an RTF document, ora PDF document.
 12. The system of claim 10, wherein the print-readydigital source document comprises a fixed layout file.
 13. The system ofclaim 10, wherein at least one of the tabular score, the narrative scoreor the label score of each of the one or more wrapping regions arecalculated based on a normalization ratio, wherein the normalizationratio is a number of normalized text fragments divided by the totalnumber of text fragments within a wrapping region.
 14. The system ofclaim 10, wherein at least one of the tabular score, the narrative scoreor the label score of each of the one or more wrapping regions arecalculated based on a density ratio, wherein the density ratio is apercentage of a bounding box area of a wrapping region occupied by textfragment bounding boxes within the wrapping region.
 15. The system ofclaim 10, wherein at least one of the tabular score, the narrative scoreor the label score of each of the one or more wrapping regions arecalculated based on an alignment ratio, wherein the alignment ratio is anumber of normalized text fragments that fit within at least onealignment group divided by a total number of normalized text fragmentswithin a wrapping region.
 16. The system of claim 10, wherein at leastone of the tabular score, the narrative score or the label score of eachof the one or more wrapping regions are calculated based on a capital ornon-alphabetic ratio, wherein the capital or non-alphabetic ratio is anumber of normalized text fragments that start with either a capitalletter or a non-alphabetic character divided by a number of normalizedtext fragments within a wrapping region.
 17. The system of claim 10,wherein at least one of the tabular score, the narrative score or thelabel score of each of the one or more wrapping regions are calculatedbased on a text fragment quantity, wherein the text fragment quantity isa number of text fragments within a wrapping region.
 18. The system ofclaim 10, wherein at least one of the tabular score, the narrative scoreor the label score of each of the one or more wrapping regions arecalculated based on a bold count, wherein the bold count is the numberof text fragments with bolded text within a wrapping region.
 19. Anon-transitory computer-readable medium including one or more sequencesof instructions which, when executed by one or more processors, causes:one or more processors; one or more non-transitory computer-readablemedia including one or more sequences of instructions which, whenexecuted by the one or more processors, causes: receiving, by acomputing system, a print-ready digital source document, the digitalsource document comprising at least one rendered page; generating, bythe computing system, one or more wrapping regions based on the digitalsource document, wherein each wrapping region comprises one or morefragment runs, and wherein each fragment run comprises one or more textfragments of the digital source document that are adjacent to oneanother and within a horizontal separation threshold and a verticalseparation threshold; classifying, by the computing system, each of theone or more wrapping regions, wherein classifying each of the one ormore wrapping regions comprises calculating, for each of the one or morewrapping regions, a tabular score, a narrative score, and a label score,wherein each of the tabular score, the narrative score, and the labelscore is a measure of qualification of a wrapping region as a tabularblock type, as a narrative block type, and as a label block type,respectively; generating, by the computing system, one or more blocks ofwrapping regions based on the classifications of the one or morewrapping regions; generating, by the computing system, one or moretables based on one or more blocks of wrapping regions; and providing,by the computing system, an electronic document comprising the one ormore tables.
 20. The non-transitory computer-readable medium of claim19, wherein the print-ready digital source document is at least one ofan XPS document, an RTF document, or a PDF document.
 21. Thenon-transitory computer-readable medium of claim 19, wherein theprint-ready digital source document comprises a fixed layout file. 22.The non-transitory computer-readable medium of claim 19, wherein atleast one of the tabular score, the narrative score or the label scoreof each of the one or more wrapping regions are calculated based on anormalization ratio, wherein the normalization ratio is a number ofnormalized text fragments divided by the total number of text fragmentswithin a wrapping region.
 23. The non-transitory computer-readablemedium of claim 19, wherein at least one of the tabular score, thenarrative score or the label score of each of the one or more wrappingregions are calculated based on a density ratio, wherein the densityratio is a percentage of a bounding box area of a wrapping regionoccupied by text fragment bounding boxes within the wrapping region. 24.The non-transitory computer-readable medium of claim 19, wherein atleast one of the tabular score, the narrative score or the label scoreof each of the one or more wrapping regions are calculated based on analignment ratio, wherein the alignment ratio is a number of normalizedtext fragments that fit within at least one alignment group divided by atotal number of normalized text fragments within a wrapping region. 25.The non-transitory computer-readable medium of claim 19, wherein atleast one of the tabular score, the narrative score or the label scoreof each of the one or more wrapping regions are calculated based on acapital or non-alphabetic ratio, wherein the capital or non-alphabeticratio is a number of normalized text fragments that start with either acapital letter or a non-alphabetic character divided by a number ofnormalized text fragments within a wrapping region.
 26. Thenon-transitory computer-readable medium of claim 19, wherein at leastone of the tabular score, the narrative score or the label score of eachof the one or more wrapping regions are calculated based on a textfragment quantity, wherein the text fragment quantity is a number oftext fragments within a wrapping region.
 27. The non-transitorycomputer-readable medium of claim 19, wherein at least one of thetabular score, the narrative score or the label score of each of the oneor more wrapping regions are calculated based on a bold count, whereinthe bold count is the number of text fragments with bolded text within awrapping region.